Daily Papers

Stresses caused by thermal expansion coefficients mismatch of anodically bonded glass and silicon samples are studied. An analytical model to determine graphically the optimum bonding temperature is presented. It is essentially a model for thin-film stress but uses integration of the linear coefficient of thermal expansion difference between the bonding temperature and the working temperature. Recommendations for choosing the bonding temperature of silicon to two brands of glass (Corning 7740 and LK5) are made. Keywords: anodic bonding, field assisted bonding, thermal expansion, stress.

During electrostatic bonding, also known as anodic bonding, silicon is bonded to glass by applying an external voltage and simultaneous heating to temperatures of 200...450 °C. While cooling to working temperature after bonding happened pieces are mutually deformed. Due to linear thermal expansion coefficients mismatch of anodically bonded glass and silicon samples an internal stress state is generated. Such stresses are called thermal mismatch stresses. The aim of this paper is a determination of technological and design solutions to achieve minimal thermal mismatch stresses in resulting bond. The nonlinear dependence of linear thermal expansion coefficients of bonded samples' materials on temperature makes it difficult to minimize thermal mismatch stresses by chosing materials with close average thermal expansion coefficients in particular temperature range. To assess means of lowering thermal mismatch stress in this paper two different ways to describe assembly are used: two thin bonded layers and multilayered composite material. Based on properties of two brands of glass (LK5, Borofloat 33) and silicon used with described mathematical models thermal mismatch stresses at temperature T_w in samples bonded at several different temperatures T_b are evaluated. Bonded silicon surface stress dependence of glass to silicon wafer thickness ratio is evaluated. Based on such evaluations one can say that by varying thickness of glass bonded to silicon one can obtain zero thermal mismatch stress at a particular depth of material or obtain stress of some defined value at this depth. Models of assembly description used in this paper can be used to optimize anodic bonding process parameters. Such usage aimed to minimize thermal mismatch stresses at device working temperatures is presented in this paper.

The goal of this work is a scientifically justified choice of efficient technological parameters of anodic bonding of silicon to glass, ensuring a minimum level of residual stresses. The relevance of this dissertation is determined by the continuous efforts to reduce the negative effects of the joint use of dissimilar materials in electronic devices. Theoretical and practical results: the temperature dependence of the true values of the coefficient of thermal expansion of popular glass brands in the temperature range from minus 100 °C to 500 °C were obtained; the ratio of thicknesses of silicon and glass that minimizes residual stress on the silicon surface was calculated; the technique of minimizing the residual stresses in silicon anodically bonded to glass was developed. Results of this research are applicable to MEMS devices design and simulations. The first chapter provides the analysis of the current state of research in the field of anodic bonding technology application. The second chapter is devoted to the experimental study of temperature dependence of the linear thermal expansion coefficient of the glass. Thermomechanical properties and composition of the following glass brands: LK5, Schott Borofloat 33, Corning 7740, Hoya SD-2 were measured. The third chapter describes the existing models of estimating the residual stresses in the silicon-glass bonds. Two models based on a theory of strength of materials and the classical lamination theory with an additional temperature dependence of coefficients of thermal expansion are proposed for further use. Residual stresses in silicon-glass bonds are estimated with these models and data from the preceding chapter. The fourth chapter describes the experiments carried out to confirm claims from the previous chapters. The proposed technique of minimizing residual stresses is described as steps for a device designer.

Processing results of measurements of linear thermal expansion coefficients and linear thermal expansion of two brands of borosilicate glasses --- LK5 and Borofloat 33 --- are presented. The linear thermal expansion of glass samples have been determined in the temperature range 130 to 800 K (minus 143 to 526 °C) using thermomechanical analyzer TMA7100. Relative imprecision of indirectly measured linear thermal expansion coefficients and linear thermal expansion of both glass brands is less than pm5 % and pm3 % respectively. Polynomial equations, approximating temperature dependence of obtained measurements, are presented. The results will facilitate the modeling of the characteristics of the devices which are used in microsystems engineering and fabricated by anodic bonding of silicon to glass, they can also be used to optimize the temperature regime of silicon to glass bonding process. Keywords: linear expansion, thermal expansion coefficient, thermomechanical analysis, borosilicate glass.

The residual stress induced in assembly is a common concern in electronic packaging. The mismatch in coefficient of thermal expansion between borosilicate glass and silicon, upon temperature variation, generates an internal stress state. This affects important characteristics of microelectromechanical devices or constituent elements. Such as self frequence or stiffness. Stresses caused by thermal expansion coefficients mismatch of anodically bonded glass and silicon samples are studied in this paper. Stress calculation based on lamination theory is presented. Usage examples of such calculations are described. For bonded silicon and LK-5 glass several results of calculations are presented. Stress distribution in bonded silicon and glass of several thicknesses is evaluated. Stress distribution in bonded glass-silicon-glass structure is evaluated. Bonded silicon surface stress dependence of glass to silicon wafer thickness ratio is evaluated. Experimental study of thermal mismatch stress in glass based on birefringence phenomenon was conducted. It's results are presented in this paper. Keywords: anodic bonding, field assisted bonding, thermal expansion, stress.

EXAFS studies at the As K edge as a function of temperature have been carried out in SmFeAsO_{1-x}F_x (x = 0 and 0.2) compounds to understand the role of local structural distortions superconductivity observed in F doped compound. A significant correlation between thermal variation of local structural parameters like anion height and superconducting onset is found in the fluorinated compound. Such a variation in anion height is absent in non-superconducting compound. Increase in Fe-As bond distance just below superconducting onset temperature indicates a similarity in distortions observed in high T_C cuprates and these Fe based superconductors.

The finite-temperature magnetic properties of Fe_xPd_{1-x} and Co_xPt_{1-x} alloys have been investigated. It is shown that the temperature-dependent magnetic behaviour of alloys, composed of originally magnetic and non-magnetic elements, cannot be described properly unless the coupling between magnetic moments at magnetic atoms (Fe,Co) mediated through the interactions with induced magnetic moments of non-magnetic atoms (Pd,Pt) is included. A scheme for the calculation of the Curie temperature (T_C) for this type of systems is presented which is based on the extended Heisenberg Hamiltonian with the appropriate exchange parameters J_{ij} obtained from {\em ab-initio} electronic structure calculations. Within the present study the KKR Green's function method has been used to calculate the J_{ij} parameters. A comparison of the obtained Curie temperatures for Fe_xPd_{1-x} and Co_xPt_{1-x} alloys with experimental data shows rather good agreement.

We report on studies of the superconducting and normal state properties of the noncentrosymmetric superconductor BeAu under hydrostatic pressure conditions. The room-temperature equation of state (EOS) reveals the values of the bulk modulus (B_0) and its first derivative (B^prime_0) at ambient pressure to be B_0 simeq 132~GPa and B^prime_0 simeq 30, respectively. Up to the highest pressures studied (p simeq 2.2~GPa), BeAu remains a multi-gap type-I superconductor. The analysis of B_{rm c}(T, p) data within the self-consistent two-gap approach suggests the presence of two superconducting energy gaps, with the gap-to-T_{rm c} ratios Δ_1/k_{rm B}T_{rm c} sim 2.3 and Δ_2/k_{rm B}T_{rm c} sim 1.1 for the larger and smaller gaps, respectively [Δ= Δ(0) is the zero-temperature value of the gap and k_{rm B} is the Boltzmann constant]. With increasing pressure, Δ_1/k_{rm B}T_{rm c} increases while Δ_2/k_{rm B}T_{rm c} decreases, suggesting that pressure enhances (weakens) the coupling strength between the superconducting carriers within the bands where the larger (smaller) superconducting energy gap has opened. The superconducting transition temperature T_{rm c}, black{the zero-temperature values of the superconducting gaps Δ_1 and Δ_2} and the zero-temperature value of the thermodynamic critical field B_{rm c}(0) decrease with increasing pressure, with the rates of {rm d}T_{rm c}/{rm d}p simeq -0.195~K/GPa, black{{rm d}Δ_1/{rm d}p simeq -0.034~meV/GPa, {rm d}Δ_2/{rm d}p simeq -0.029~meV/GPa,} and {rm d}B_{rm c}(0)/{rm d}p = -2.65(1)~mT/GPa, respectively. The measured B_{rm c}(0) values plotted as a function of T_{rm c} follow an empirical scaling relation established for conventional type-I superconductors.

It has been proposed that the superconducting transition temperature T_{c} of an unconventional superconductor with a large pairing scale but strong phase fluctuations can be enhanced by coupling it to a metal. However, the general efficacy of this approach across different parameter regimes remains an open question. Using the dynamical cluster approximation, we study this question in a system composed of an attractive Hubbard layer in the intermediate coupling regime, where the magnitude of the attractive Coulomb interaction |U| is slightly larger than the bandwidth W, hybridized with a noninteracting metallic layer. We find that while the superconducting transition becomes more mean-field-like with increasing interlayer hopping, the superconducting transition temperature T_{c} exhibits a nonmonotonic dependence on the strength of the hybridization t_{perp}. This behavior arises from a reduction of the effective pairing interaction in the correlated layer that out-competes the growth in the intrinsic pair-field susceptibility induced by the coupling to the metallic layer. We find that the largest T_{c} inferred here for the composite system is below the maximum value currently estimated for the isolated negative-U Hubbard model.

This paper presents a modeling effort to explore the underlying physics of temperature evolution during additive friction stir deposition (AFSD) by a human-AI teaming approach. AFSD is an emerging solid-state additive manufacturing technology that deposits materials without melting. However, both process modeling and modeling of the AFSD tool are at an early stage. In this paper, a human-AI teaming approach is proposed to combine models based on first principles with AI. The resulting human-informed machine learning method, denoted as AFSD-Physics, can effectively learn the governing equations of temperature evolution at the tool and the build from in-process measurements. Experiments are designed and conducted to collect in-process measurements for the deposition of aluminum 7075 with a total of 30 layers. The acquired governing equations are physically interpretable models with low computational cost and high accuracy. Model predictions show good agreement with the measurements. Experimental validation with new process parameters demonstrates the model's generalizability and potential for use in tool temperature control and process optimization.

We present two new interatomic potentials for platinum (Pt) in angular-dependent potential (ADP) and modified Tersoff (MT) formats. Both potentials have been trained on a reference database of first-principles calculations without using experimental data. The properties of Pt predicted by the ADP and MT potentials agree better with DFT calculations and experimental data than the potentials available in the literature. Future applications of the MT model to mixed-bonding metal-covalent systems are discussed.

A new multiferroic material, CuBr2, is reported for the first time. CuBr2 has not only a high transition temperature (close to liquid nitrogen temperature) but also low dielectric loss and strong magnetoelectric coupling. These findings reveal the importance of anion effects in the search for the high temperature multiferroics materials among these low-dimensional spin systems.

In order to make accurate predictions of material properties, current machine-learning approaches generally require large amounts of data, which are often not available in practice. In this work, an all-round framework is presented which relies on a feedforward neural network, the selection of physically-meaningful features and, when applicable, joint-learning. Next to being faster in terms of training time, this approach is shown to outperform current graph-network models on small datasets. In particular, the vibrational entropy at 305 K of crystals is predicted with a mean absolute test error of 0.009 meV/K/atom (four times lower than previous studies). Furthermore, joint-learning reduces the test error compared to single-target learning and enables the prediction of multiple properties at once, such as temperature functions. Finally, the selection algorithm highlights the most important features and thus helps understanding the underlying physics.

Nonlinear elastic properties of polymers and polymeric composites are essential for accurate prediction of their response to dynamic loads, which is crucial in a wide range of applications. These properties can be affected by strain rate, temperature, and pressure. The temperature susceptibility of nonlinear elastic moduli of polymers remains poorly understood. We have recently observed a significant frequency dependence of the nonlinear elastic (Murnaghan) moduli of polystyrene. In this paper we expand this analysis by the temperature dependence. The measurement methodology was based on the acousto-elastic effect, and involved analysis of the dependencies of velocities of longitudinal and shear single-frequency ultrasonic waves in the sample on the applied static pressure. Measurements were performed at different temperatures in the range of 25-65 {\deg}C and at different frequencies in the range of 0.75-3 MHz. The temperature susceptibility of the nonlinear moduli l and m was found to be two orders of magnitude larger than that of linear moduli lambda and mu. At the same time, the observed variations of n modulus with temperature were low and within the measurement tolerance. The observed tendencies can be explained by different influence of pressure on relaxation processes in the material at different temperatures.

The independent atom ansatz of density functional theory yields an accurate analytical expression for dynamic correlation energy in the H_{2} molecule: E_{c} = 0.5(1 - 2)(ab|ba) for the atom-additive self-consistent density rho = |a|^{2} + |b|^{2}. Combined with exact atomic self-exchange, it recovers more than 99.5 % of nearly exact SCAN exchange-correlation energy at R > 0.5 A, differing by less than 0.12 eV. The total energy functional correctly dissociates the H-H bond and yields absolute errors of 0.002 A, 0.19 eV, and 13 cm^{-1} relative to experiment at the tight binding computational cost. The chemical bond formation is attributed to the asymptotic Heitler-London resonance of quasi-orthogonal atomic states (- (ab|ba)) with no contributions from kinetic energy or charge accumulation in the bond.

The temperature parameter plays a profound role during training and/or inference with large foundation models (LFMs) such as large language models (LLMs) and CLIP models. Particularly, it adjusts the logits in the softmax function in LLMs, which is crucial for next token generation, and it scales the similarities in the contrastive loss for training CLIP models. A significant question remains: Is it viable to learn a neural network to predict a personalized temperature of any input data for enhancing LFMs"? In this paper, we present a principled framework for learning a small yet generalizable temperature prediction network (TempNet) to improve LFMs. Our solution is composed of a novel learning framework with a robust loss underpinned by constrained distributionally robust optimization (DRO), and a properly designed TempNet with theoretical inspiration. TempNet can be trained together with a large foundation model from scratch or learned separately given a pretrained foundation model. It is not only useful for predicting personalized temperature to promote the training of LFMs but also generalizable and transferable to new tasks. Our experiments on LLMs and CLIP models demonstrate that TempNet greatly improves the performance of existing solutions or models, e.g. Table 1. The code to reproduce the experimental results in this paper can be found at https://github.com/zhqiu/TempNet.

The optical properties of the superconducting K_{0.8}Fe_{1.7}(Se_{0.73}S_{0.27})_2 single crystals with a critical temperature T_capprox 26 K have been measured in the {\it ab} plane in a wide frequency range using both infrared Fourier-transform spectroscopy and spectroscopic ellipsometry at temperatures of 4--300 K. The normal-state reflectance of K_{0.8}Fe_{1.7}(Se_{0.73}S_{0.27})_2 is analyzed using a Drude-Lorentz model with one Drude component. The temperature dependences of the plasma frequency, optical conductivity, scattering rate, and dc resistivity of the Drude contribution in the normal state are presented. In the superconducting state, we observe a signature of the superconducting gap opening at 2Δ(5~K) = 11.8~meV. An abrupt decrease in the low-frequency dielectric permittivity varepsilon _1(ω) at T < T_c also evidences the formation of the superconducting condensate. The superconducting plasma frequency ω_{pl,s} = (213pm 5)~cm^{-1} and the magnetic penetration depth λ=(7.5pm 0.2)~μm at T=5~K are determined.

Technologies that function at room temperature often require magnets with a high Curie temperature, T_C, and can be improved with better materials. Discovering magnetic materials with a substantial T_C is challenging because of the large number of candidates and the cost of fabricating and testing them. Using the two largest known data sets of experimental Curie temperatures, we develop machine-learning models to make rapid T_C predictions solely based on the chemical composition of a material. We train a random forest model and a k-NN one and predict on an initial dataset of over 2,500 materials and then validate the model on a new dataset containing over 3,000 entries. The accuracy is compared for multiple compounds' representations ("descriptors") and regression approaches. A random forest model provides the most accurate predictions and is not improved by dimensionality reduction or by using more complex descriptors based on atomic properties. A random forest model trained on a combination of both datasets shows that cobalt-rich and iron-rich materials have the highest Curie temperatures for all binary and ternary compounds. An analysis of the model reveals systematic error that causes the model to over-predict low-T_C materials and under-predict high-T_C materials. For exhaustive searches to find new high-T_C materials, analysis of the learning rate suggests either that much more data is needed or that more efficient descriptors are necessary.

The tradeoff between precision and performance in molecular simulations can nowadays be addressed by machine-learned force fields (MLFF), which combine ab initio accuracy with force field numerical efficiency. Different from conventional force fields however, incorporating relevant electronic degrees of freedom into MLFFs becomes important. Here, we implement an equivariant transformer that embeds molecular net charge and spin state without additional neural network parameters. The model trained on a singlet/triplet non-correlated CH2 dataset can identify different spin states and shows state-of-the-art extrapolation capability. Therein, self-attention sensibly captures non-local effects, which, as we show, can be finely tuned over the network hyper-parameters. We indeed found that Softmax activation functions utilised in the self-attention mechanism of graph networks outperformed ReLU-like functions in prediction accuracy. Increasing the attention temperature from τ= d to 2d further improved the extrapolation capability, indicating a weighty role of nonlocality. Additionally, a weight initialisation method was purposed that sensibly accelerated the training process.

LLM-as-judge ("grader") components are now standard in evaluation harnesses, including safety evaluations where a pass/fail verdict may gate downstream deployment decisions. A widespread assumption is that setting the grader's sampling temperature to 0 makes grading deterministic. We test this assumption against a real safety-evaluation codebase (Japan AISI's open-source aisev) and show it fails on two levels. First, the harness invokes its grader without setting temperature or seed; the underlying provider silently applies its default of 1.0, so items near the decision boundary flip pass/fail across identical runs (per-item disagreement up to ~50% over 20 runs). Second, pinning temperature=0 reduces but does not eliminate flips: across 690 API calls spanning two providers, three model tiers, and five sampling configurations, 1-2 of 7 borderline items remain non-reproducible even under forced greedy decoding (top_k=1). Claude Opus 4.7/4.8 has since deprecated temperature entirely, rendering the primary mitigation inapplicable to newer model generations. These findings expose a structural gap: evaluation harnesses that report single-run verdicts without variance or grader-disagreement metrics can present noise as a safety property. We release a reproduction harness (690 calls, 7 conditions) and recommend that harnesses treat grader disagreement as a first-class health metric alongside the scores themselves.

Phase stability in multicomponent solid solutions depends on configurational entropy beyond the ideal mixing limit, but capturing it together with vibrational entropy within the same atomistic framework remains challenging. Here, we extend non-equilibrium thermodynamic integration to composition-dependent transformations through an alchemical interpolation of the interactions, combined with Monte Carlo identity exchange moves and molecular dynamics that sample the vibrational and non-ideal configurational entropy along the integration path. We apply the framework to the Au-Cu binary alloy using Atomic Cluster Expansion potentials trained on density functional theory data using the LDA, PBE, and r2SCAN functionals, and construct composition-temperature phase diagrams directly from atomistic free energies. We find that explicit configurational sampling lowers the AuCu order-disorder transition temperature predicted by the ACE potential trained on LDA data from approximately 810 K to 710 K, closer to the experimental value of 683 K, and substantially widens the stability range of the solid solution. At the same time, the much larger sensitivity to the exchange-correlation functional shows that this level of agreement should not be interpreted as general predictive accuracy. Non-ideal configurational entropy must therefore be sampled explicitly, alongside a careful choice of functional, for a reliable atomistic description of binary phase diagrams.

This article presents a Monte Carlo study on bond percolation in distorted square and triangular lattices. The distorted lattices are generated by dislocating the sites from their regular positions. The amount and direction of the dislocations are random, but can be tuned by the distortion parameter α. Once the sites are dislocated, the bond lengths δ between the nearest neighbors change. A bond can only be occupied if its bond length is less than a threshold value called the connection threshold d. It is observed that when the connection threshold is greater than the lattice constant (assumed to be 1), the bond percolation threshold p_b always increases with distortion. For dle 1, no spanning configuration is found for the square lattice when the lattice is distorted, even very slightly. On the other hand, the triangular lattice not only spans for dle 1, it also shows a decreasing trend for p_b in the low-α range. These variation patterns have been linked with the average coordination numbers of the distorted lattices. A critical value d_c for the connection threshold has been defined as the value of d below which no spanning configuration can be found even after occupying all the bonds satisfying the connection criterion δle d. The behavior of d_c(α) is markedly different for the two lattices.

Liquid nitrogen (LN2) is a long-time favorite for physics demonstrations, with a large repertoire of crowd-pleasing experiments that are cornerstones in outreach efforts. While R1 universities usually have a ready LN2 supply for their Physics, Chemistry, and Biology departments, K-12 and two-year college teachers often have to go to specialty suppliers to obtain LN2, and also need access to expensive storage equipment. Dry ice (solid CO2) is available at many super markets, and as such its suitability as a substitute for LN2 was explored, with the results discussed below. At 77K (-196{\deg}C) LN2 is considerably colder than dry ice at 195K (-78.5{\deg}C), however some demonstrations are still viable.

The structural stability and phonon properties of SnSe/SnS superlattices at finite temperatures have been studied using machine learning force field molecular dynamics and the anharmonic phonon approach. The vertical SnSe/SnS superlattice undergoes a phase transition from the Pnma phase to a novel P4/nmm phase at finite temperatures, which is different from the high-temperature Cmcm phase of the SnSe and SnS systems. The stability of P4/nmm phase is determined by molecular dynamics trajectories and anharmonic phonon dispersion relations. The imaginary modes of TO modes at the q=M(1/2,1/2,0) point of the P4/nmm phase in harmonic approximation become rigid at elevated temperatures. An analysis of phonon power spectra upon temperature also confirms the dynamic stabilization. The P4/nmm phase has higher symmetry than the Pnma phase, and the phase transition between them is accompanied by competition between the Jahn-Teller effect and phonon anharmonicity. Unlike the anisotropic distribution of Sn-Se/S bonds in the Pnma phase, the P4/nmm phase forms chemical bonds with similar bond lengths both in-plane and interlayer, and their resonance effect can significantly enhance phonon scattering. The calculated phonon density of states and lifetime is strongly temperature dependent, demonstrating the heavy anharmonicity in the SnSe/SnS system. The P4/nmm phase has an extremely low lattice thermal conductivity, close to the experimental values of SnSe and SnS. Moreover, with the reduction of band gap and the enhancement of band degeneracy near the Fermi level, the P4/nmm phase exhibits superior electronic transport properties and significantly enhanced response to infrared and visible light. This makes it show great potential in thermoelectric and photovoltaic applications.

Organosulfur species are potential major carriers of sulfur in the interstellar medium, as well as interesting ingredients in prebiotic chemistry. The most fundamental question regarding these species is under which conditions they reside in the gas versus solid phase. Here, we characterize the thermal desorption kinetics, binding energies, and entrapment of the organosulfur methyl mercaptan (CH_3SH, or MeSH) in different ice environments, comparing them with those of methanol (CH_3OH, or MeOH) ices. The derived multi-layer (pure MeSH-MeSH) and sub-monolayer (layered MeSH-H_2O) binding energies are surprisingly similar, corresponding to snow line locations where the disk midplane temperature is ~105 K. In both H_2O-dominated and more realistic H_2O:CO_2-dominated ices, 100% of the MeSH is entrapped, almost exclusively desorbing at the molecular volcano desorption peak, indicating that MeSH is retained at the water snow line if initially mixed with water ice during formation. Additionally, the presence of MeSH in an ice mixture enhances the entrapment of CO_2 and MeOH (up to 100%) until the onset of volcano desorption; without MeSH, both desorb at their respective pure desorption temperatures and also co-desorb with water. Compared to MeOH, MeSH binds less well to water, explaining why MeSH escapes during water ice crystallization rather than co-desorbing with water. These results show the larger relative size of MeSH compared to MeOH significantly impacts its ability to bind to water and its entrapment efficiency. Therefore, molecular size plays an important role in the adsorption and retention of S-bearing organics and, in turn, other volatiles in ices.

Searching for new superconductors, especially unconventional superconductors, has been studied extensively for decades but remains one of the major outstanding challenges in condensed matter physics. Medium/high-entropy alloys (MEAs-HEAs) are new fertile soils of unconventional superconductors and generate widespread interest and questions on the existence of superconductivity in highly disordered materials. Here, we report on the effect of Ni-doped on the crystal structure and superconductivity properties of strongly coupled TiHfNbTa MEA. XRD results indicate that the maximum solid solution of (Ti1/4Hf1/4Nb1/4Ta1/4)1-xNix is about 7.7%. Resistivity, magnetic susceptibility, and specific heat measurements demonstrated that (Ti1/4Hf1/4Nb1/4Ta1/4)1-xNix HEAs are all bulk type-II superconductors and follow the trend of the increase of Tc with the increase of Ni-doped contents. The specific heat jump of all (Ti1/4Hf1/4Nb1/4Ta1/4)1-xNix are much larger than the BCS value of 1.43, suggesting all these HEAs are strongly coupled superconductors. Additionally, large Kadawaki-Woods ratio values suggest that there is a strong electron correlation effect in this system. The (Ti1/4Hf1/4Nb1/4Ta1/4)1-xNix HEA system is a new ideal material platform for the study of strong correlation behavior and strongly coupled superconductivity, which provides an insight into the physics of high-temperature superconductors or other unconventional superconductors.

We develop a multi-step workflow for the discovery of conventional superconductors, starting with a Bardeen Cooper Schrieffer inspired pre-screening of 1736 materials with high Debye temperature and electronic density of states. Next, we perform electron-phonon coupling calculations for 1058 of them to establish a large and systematic database of BCS superconducting properties. Using the McMillan-Allen-Dynes formula, we identify 105 dynamically stable materials with transition temperatures, Tc>5 K. Additionally, we analyze trends in our dataset and individual materials including MoN, VC, VTe, KB6, Ru3NbC, V3Pt, ScN, LaN2, RuO2, and TaC. We demonstrate that deep-learning(DL) models can predict superconductor properties faster than direct first principles computations. Notably, we find that by predicting the Eliashberg function as an intermediate quantity, we can improve model performance versus a direct DL prediction of Tc. We apply the trained models on the crystallographic open database and pre-screen candidates for further DFT calculations.

We investigate spectral features of bottomonium at high temperature, in particular the thermal mass shift and width of ground state S-wave and P-wave state. We employ and compare a range of methods for determining these features from lattice NRQCD correlators, including direct correlator analyses (multi-exponential fits and moments of spectral functions), linear methods (Backus-Gilbert, Tikhonov and HLT methods), and Bayesian methods for spectral function reconstruction (MEM and BR). We comment on the reliability and limitations of the various methods.

The collision system and multiplicity dependence of chemical freeze-out temperature (T_{rm ch}) and strangeness saturation factor (γ_{s}) are obtained by studying the particle ratios at the Large Hadron Collider (LHC) energies. Here, we consider the new results in pp at 13 TeV, p+Pb at s_{rm NN} = 5.02 TeV, Xe+Xe at s_{rm NN} = 5.44 TeV and Pb+Pb at s_{rm NN} = 5.02 TeV along with the earlier results in pp at s = 7 TeV and Pb+Pb at s_{rm NN} = 2.76 TeV. A statistical thermal model is used to extract the chemical freeze-out parameters in different multiplicity classes. To understand the particle production from small to large collision systems two ensembles namely, canonical and grand canonical have been considered in this study. A clear observation of multiplicity dependence of T_{rm ch} and γ_{s} is observed. The values obtained in high-multiplicity pp collisions are found to be similar to the peripheral Pb+Pb collisions. A final state midrapidity charged particle multiplicity density of around 20-30 appears to be a threshold below which, the chemical freeze-out temperature is lower than the kinetic freeze-out temperature.

Near-surface air temperature is a key physical property of the Earth's surface. Although weather stations offer continuous monitoring and satellites provide broad spatial coverage, no single data source offers seamless data in a spatiotemporal fashion. Here, we propose a data-driven, physics-guided deep learning approach to generate hourly air temperature data at 2 km resolution over the contiguous United States. The approach, called Amplifier Air-Transformer, first reconstructs GOES-16 surface temperature data obscured by clouds. It does so through a neural network encoded with the annual temperature cycle, incorporating a linear term to amplify ERA5 temperature values at finer scales and convolutional layers to capture spatiotemporal variations. Then, another neural network transforms the reconstructed surface temperature into air temperature by leveraging its latent relationship with key Earth surface properties. The approach is further enhanced with predictive uncertainty estimation through deep ensemble learning to improve reliability. The proposed approach is built and tested on 77.7 billion surface temperature pixels and 155 million air temperature records from weather stations across the contiguous United States (2018-2024), achieving hourly air temperature mapping accuracy of 1.93 C in station-based validation. The proposed approach streamlines surface temperature reconstruction and air temperature prediction, and it can be extended to other satellite sources for seamless air temperature monitoring at high spatiotemporal resolution. The generated data of this study can be downloaded at https://doi.org/10.5281/zenodo.15252812, and the project webpage can be found at https://skrisliu.com/HourlyAirTemp2kmUSA/.

The rapid discovery of materials is constrained by the lack of large, machine-readable datasets that couple performance metrics with structural context. Existing databases are either small, manually curated, or biased toward first principles results, leaving experimental literature underexploited. We present an agentic, large language model (LLM)-driven workflow that autonomously extracts thermoelectric and structural-properties from about 10,000 full-text scientific articles. The pipeline integrates dynamic token allocation, zeroshot multi-agent extraction, and conditional table parsing to balance accuracy against computational cost. Benchmarking on 50 curated papers shows that GPT-4.1 achieves the highest accuracy (F1 = 0.91 for thermoelectric properties and 0.82 for structural fields), while GPT-4.1 Mini delivers nearly comparable performance (F1 = 0.89 and 0.81) at a fraction of the cost, enabling practical large scale deployment. Applying this workflow, we curated 27,822 temperature resolved property records with normalized units, spanning figure of merit (ZT), Seebeck coefficient, conductivity, resistivity, power factor, and thermal conductivity, together with structural attributes such as crystal class, space group, and doping strategy. Dataset analysis reproduces known thermoelectric trends, such as the superior performance of alloys over oxides and the advantage of p-type doping, while also surfacing broader structure-property correlations. To facilitate community access, we release an interactive web explorer with semantic filters, numeric queries, and CSV export. This study delivers the largest LLM-curated thermoelectric dataset to date, provides a reproducible and cost-profiled extraction pipeline, and establishes a foundation for scalable, data-driven materials discovery beyond thermoelectrics.

Data-driven methods, in particular machine learning, can help to speed up the discovery of new materials by finding hidden patterns in existing data and using them to identify promising candidate materials. In the case of superconductors, which are a highly interesting but also a complex class of materials with many relevant applications, the use of data science tools is to date slowed down by a lack of accessible data. In this work, we present a new and publicly available superconductivity dataset ('3DSC'), featuring the critical temperature T_c of superconducting materials additionally to tested non-superconductors. In contrast to existing databases such as the SuperCon database which contains information on the chemical composition, the 3DSC is augmented by the approximate three-dimensional crystal structure of each material. We perform a statistical analysis and machine learning experiments to show that access to this structural information improves the prediction of the critical temperature T_c of materials. Furthermore, we see the 3DSC not as a finished dataset, but we provide ideas and directions for further research to improve the 3DSC in multiple ways. We are confident that this database will be useful in applying state-of-the-art machine learning methods to eventually find new superconductors.

We address the problem of uncertainty calibration and introduce a novel calibration method, Parametrized Temperature Scaling (PTS). Standard deep neural networks typically yield uncalibrated predictions, which can be transformed into calibrated confidence scores using post-hoc calibration methods. In this contribution, we demonstrate that the performance of accuracy-preserving state-of-the-art post-hoc calibrators is limited by their intrinsic expressive power. We generalize temperature scaling by computing prediction-specific temperatures, parameterized by a neural network. We show with extensive experiments that our novel accuracy-preserving approach consistently outperforms existing algorithms across a large number of model architectures, datasets and metrics.

The NMR measurements performed on a single orthorhombic crystal of superconducting ferromagnet UCoGe (Y.Ihara et al, Phys. Rev. Lett. v.105, 206403 (2010)) demonstrate strongly anisotropic magnetic properties of this material. The presented calculations allow to establish the dependence of longitudinal spin-lattice relaxation rate from temperature and magnetic field. The value 1/T_1T in field perpendicular to spontaneous magnetisation directed along c-axis has maximum in vicinity of Curie temperature whereas it does not reveal similar behaviour in field parallel to the direction of spontaneous magnetisation. Also there was shown that the longitudinal spin-lattice relaxation rate is strongly field dependent when the field directed in b-crystallographic direction but field independent if magnetic field is oriented along a-axis.

A detailed study of published experimental data for a variety of materials on the incremental variation of heat capacity, thermal expansion, and compressibility at glassy freezing reveals a striking dependence of the Prigogine-Defay ratio R on the fragility index m. At high m, R approaches values of ~1, the Ehrenfest expectation for 2nd order continuous phase transitions, while R reaches values > 20 for low fragilities. We explain this correlation by the degree of separation of the glassy freezing temperature from a hidden phase transition into an ideal low-temperature glass.

Electron-electron (e-e) and electron-phonon (e-ph) interactions are challenging to describe in correlated materials, where their joint effects govern unconventional transport, phase transitions, and superconductivity. Here we combine first-principles e-ph calculations with dynamical mean field theory (DMFT) as a step toward a unified description of e-e and e-ph interactions in correlated materials. We compute the e-ph self-energy using the DMFT electron Green's function, and combine it with the e-e self-energy from DMFT to obtain a Green's function including both interactions. This approach captures the renormalization of quasiparticle dispersion and spectral weight on equal footing. Using our method, we study the e-ph and e-e contributions to the resistivity and spectral functions in the correlated metal Sr_2RuO_4. In this material, our results show that e-e interactions dominate transport and spectral broadening in the temperature range we study (50-310~K), while e-ph interactions are relatively weak and account for only sim10\% of the experimental resistivity. We also compute effective scattering rates, and find that the e-e interactions result in scattering several times greater than the Planckian value k_BT, whereas e-ph interactions are associated with scattering rates lower than k_BT. Our work demonstrates a first-principles approach to combine electron dynamical correlations from DMFT with e-ph interactions in a consistent way, advancing quantitative studies of correlated materials.

The interstellar medium (ISM) is all but empty. To date, more than 300 molecules have already been discovered. Because of the extremely low temperature, the gas-phase chemistry is dominated by barrierless exothermic reactions of radicals and ions. However, several abundant molecules and organic molecules cannot be produced efficiently by gas-phase reactions. To explain the existence of such molecules in the ISM, gas-surface interactions between small molecules and dust particles covered with amorphous solid water (ASW) mantles must be considered. In general, surface processes such as adsorption, diffusion, desorption, and chemical reactions can be linked to the binding energy of molecules to the surface. Hence, a lot of studies have been performed to identify the binding energies of interstellar molecules on ASW surfaces. Cosmic radiation and free electrons may induce a negative charge on the dust particles, and the binding energies may be affected by this charge. In this study, we calculate the binding energies of CO, CH4, and NH3, on neutral and charged ASW surfaces using DFT calculations. Our results indicate that CO can interact with the surface charge, increasing its binding energy. In contrast, the binding energy of CH4 remains unchanged in the presence of surface charge, and that of NH3 typically decreases.

Machine learning interatomic potentials (MLIPs) have become increasingly effective at approximating quantum mechanical calculations at a fraction of the computational cost. However, lower errors on held out test sets do not always translate to improved results on downstream physical property prediction tasks. In this paper, we propose testing MLIPs on their practical ability to conserve energy during molecular dynamic simulations. If passed, improved correlations are found between test errors and their performance on physical property prediction tasks. We identify choices which may lead to models failing this test, and use these observations to improve upon highly-expressive models. The resulting model, eSEN, provides state-of-the-art results on a range of physical property prediction tasks, including materials stability prediction, thermal conductivity prediction, and phonon calculations.

The origin of a chemical reaction between two reactant atoms is associated to the activation energy, with the assumption that, high-energy collisions between these atoms, are the ones that overcome the activation energy. Here, we (i) show that a stronger attractive van der Waals (vdW) and electron-ion Coulomb interactions between two polarized atoms are responsible to initiate a chemical reaction, either before or after the collision. We derive this stronger vdW attraction formula exactly using the quasi one-dimensional Drude model within the ionization energy theory and the energy-level spacing renormalization group method. Along the way, we (ii) expose the precise physical mechanism responsible for the existence of a stronger vdW interaction for both long and short distances, and also show how to technically avoid the electron-electron Coulomb repulsion between polarized electrons from these two reactant atoms. Finally, we properly and correctly associate the existence of this stronger attraction to Ramachandran's 'normal limits' (distance shorter than what is allowed by the standard vdW bond) between chemically nonbonded atoms.

We report a quasi-one-dimensional S = 1 spin chain compound BaNiTe2O7. This magnetic system has been investigated by magnetic susceptibility, specific heat, and neutron powder diffraction. These results indicate that BaNiTe2O7 develops a short-range magnetic correlation around T ~ 22 K. With further cooling, an antiferromagnetic phase transition is observed at TN ~ 5.4 K. Neutron powder diffraction revealed antiferromagnetic noncollinear order with a commensurate propagation vector k = (1/2, 1, 0). The refined magnetic moment size of Ni2+ at 1.5 K is 1.84{\mu}B, and its noncollinear spin texture is confirmed by first-principles calculations. Inelastic neutron-scattering results and density functional theory calculations confirmed the quasi-one-dimensional nature of the spin systems.

The discovery of high-temperature superconducting materials holds great significance for human industry and daily life. In recent years, research on predicting superconducting transition temperatures using artificial intelligence~(AI) has gained popularity, with most of these tools claiming to achieve remarkable accuracy. However, the lack of widely accepted benchmark datasets in this field has severely hindered fair comparisons between different AI algorithms and impeded further advancement of these methods. In this work, we present the HTSC-2025, an ambient-pressure high-temperature superconducting benchmark dataset. This comprehensive compilation encompasses theoretically predicted superconducting materials discovered by theoretical physicists from 2023 to 2025 based on BCS superconductivity theory, including the renowned X_2YH_6 system, perovskite MXH_3 system, M_3XH_8 system, cage-like BCN-doped metal atomic systems derived from LaH_{10} structural evolution, and two-dimensional honeycomb-structured systems evolving from MgB_2. The HTSC-2025 benchmark has been open-sourced at https://github.com/xqh19970407/HTSC-2025 and will be continuously updated. This benchmark holds significant importance for accelerating the discovery of superconducting materials using AI-based methods.

Accurately predicting protein melting temperature changes (Delta Tm) is fundamental for assessing protein stability and guiding protein engineering. Leveraging multi-modal protein representations has shown great promise in capturing the complex relationships among protein sequences, structures, and functions. In this study, we develop models based on powerful protein language models, including ESM-2, ESM-3 and AlphaFold, using various feature extraction methods to enhance prediction accuracy. By utilizing the ESM-3 model, we achieve a new state-of-the-art performance on the s571 test dataset, obtaining a Pearson correlation coefficient (PCC) of 0.50. Furthermore, we conduct a fair evaluation to compare the performance of different protein language models in the Delta Tm prediction task. Our results demonstrate that integrating multi-modal protein representations could advance the prediction of protein melting temperatures.

The local structure of V_{2}O_{3}, an archetypal strongly correlated electron system that displays a metal-insulator transition around 160 K, has been investigated via pair distribution function (PDF) analysis of neutron and x-ray total scattering data. The rhombohedral-to-monoclinic structural phase transition manifests as an abrupt change on all length scales in the observed PDF. No monoclinic distortions of the local structure are found above the transition, although coexisting regions of phase-separated rhombohedral and monoclinic symmetry are observed between 150 K and 160 K. This lack of structural fluctuations above the transition contrasts with the known presence of magnetic fluctuations in the high-temperature state, suggesting that the lattice degree of freedom plays a secondary role behind the spin degree of freedom in the transition mechanism.

In this paper, we aim to optimize a contrastive loss with individualized temperatures in a principled and systematic manner for self-supervised learning. The common practice of using a global temperature parameter tau ignores the fact that ``not all semantics are created equal", meaning that different anchor data may have different numbers of samples with similar semantics, especially when data exhibits long-tails. First, we propose a new robust contrastive loss inspired by distributionally robust optimization (DRO), providing us an intuition about the effect of tau and a mechanism for automatic temperature individualization. Then, we propose an efficient stochastic algorithm for optimizing the robust contrastive loss with a provable convergence guarantee without using large mini-batch sizes. Theoretical and experimental results show that our algorithm automatically learns a suitable tau for each sample. Specifically, samples with frequent semantics use large temperatures to keep local semantic structures, while samples with rare semantics use small temperatures to induce more separable features. Our method not only outperforms prior strong baselines (e.g., SimCLR, CLIP) on unimodal and bimodal datasets with larger improvements on imbalanced data but also is less sensitive to hyper-parameters. To our best knowledge, this is the first methodical approach to optimizing a contrastive loss with individualized temperatures.

The amplitude of ground state superconducting energy gap Δ(0) and relative jump in electronic specific heat at the transition temperature, ΔC{/}γT_c, are primary fundamental parameters of any superconductor. There are several well-established techniques to measure these values for bulk samples. However, there is limited number of techniques which can be applied to measure these parameters in atomically thin superconductors. Here we proposed a new approach to extract Δ(0) and ΔC{/}γT_c in atomically thin superconductors by utilizing perpendicular, Bc2,perp(T) (when magnetic field is applied in perpendicular direction to the film surface), and parallel, Bc2,||(T) (when magnetic field is applied in parallel direction to the film surface), upper critical field data. Deduced parameters for few layers thick Al, Sn, NbSe2, MoS2, magic angle twisted trilayer graphene (MATTG), and WTe2 are well matched values expected for strong- and moderately strong-coupled electron-phonon mediated superconductors. Observed, in many atomically thin superconductors, an enhancement of Bc2,||(0) above the Pauli-Clogston-Chandrasekhar limiting field (i.e., magnetic field required to break the Cooper pair) is explained based on the sample geometry, without an assumption that some exotic pairing mechanism, for instance, Ising-type, is emergent in these materials.

Recent experiments on 2D superconductors allow the characterization of the critical temperature and of the phase diagram across the BCS-BEC crossover as a function of density. We obtain from these experiments the microscopic parameters of the superconducting state at low temperatures by the BCS mean-field approach. For Li_xZrNCl, the extracted parameters are used to evaluate the superconducting phase stiffness and the Berezinskii-Kosterlitz-Thouless (BKT) critical temperature throughout the BCS-BEC crossover, by implementing the corresponding Renormalization Group (RG) approach. In this way, we make a quantitative test of the predictive power of the BKT theory for evaluating the critical temperature. The RG flow equations turn out to give a sizable renormalization of the phase stiffness and of the critical temperature, which is crucial to obtain a satisfactory agreement between the BKT theory and the experiments, in particular in the BCS-BEC crossover regime. We predict the temperature range where phase stiffness renormalization can be measured in Li_xZrNCl across the BCS-BEC crossover. Contrary to other microscopic theories of superconductivity, we find that the BKT theory can be exploited to evaluate quantitatively the critical temperature of 2D superconductors in different pairing regimes.

The demand for safe, high-energy-density batteries has spotlighted halide solid-state electrolytes, which offer the potential for enhanced ionic mobility, electrochemical stability, and interfacial deformability. Accelerating their discovery requires extensive molecular dynamics, which has been increasingly enabled by universal machine learning interatomic potentials trained on foundational datasets. However, the dynamic softness of halides poses a stringent test of whether general-purpose models can reliably replace first-principles calculations under the highly distorted, elevated-temperature regimes necessary to probe ion transport. Here, we present AQVolt26, a dataset of 322,656 r^2SCAN single-point calculations for lithium halides, generated via high-temperature configurational sampling across sim5K structures. We demonstrate that foundational datasets provide a strong baseline for stable halide chemistries and transfer local forces well, however absolute energy predictions degrade in distorted higher-temperature regimes. Co-training with AQVolt26 resolves this blind spot. Furthermore, incorporating Materials Project relaxation data improves near-equilibrium performance but degrades extreme-strain robustness without enhancing high-temperature force accuracy. These results demonstrate that domain-specific configurational sampling is essential for the reliable dynamic screening of halide electrolytes. Furthermore, our findings suggest that while foundational models provide a robust base, they are most effective for dynamically soft solid-state chemistries when augmented with targeted, high-temperature data. Finally, we show that near-equilibrium relaxation data serves as a task-specific complement rather than a universally beneficial addition.

A model to investigate Thermally Stimulated Depolarization Current (TSDC) peak parameters using the dipole-dipole interaction concept is proposed by the author in this work. The proposed model describe the (TSDC) peak successfully since it gives a significant peak parameters (i.e. Activation energy (E) and the per-exponential factor (\tau_0) in addition to the dipole-dipole interaction strength parameter (di). Application of this model to determine the peak parameters of polyvinyl chloride(PVC) polymer is presented . The results show how the model fit the experimental thermal sampling data. Finally the results are compared to the well know techniques; the initial rise method (IR), the half width method (HW) in addition to the Cowell and Woods analysis.

We establish a fundamental connection between score-based diffusion models and non-equilibrium thermodynamics by deriving performance limits based on entropy rates. Our main theoretical contribution is a lower bound on the negative log-likelihood of the data that relates model performance to entropy rates of diffusion processes. We numerically validate this bound on a synthetic dataset and investigate its tightness. By building a bridge to entropy rates - system, intrinsic, and exchange entropy - we provide new insights into the thermodynamic operation of these models, drawing parallels to Maxwell's demon and implications for thermodynamic computing hardware. Our framework connects generative modeling performance to fundamental physical principles through stochastic thermodynamics.

The acceleration of the young solar wind is studied using the first 17 encounters of Parker Solar Probe. We identify wind intervals from different source regions: coronal hole (CH) interiors, streamers, and low Mach number boundary layers (LMBLs), i.e. the inner boundaries of coronal holes. We present their statistical trends in the acceleration process. Most of the observations can be reproduced by a two-fluid hydrodynamic model with realistic corona temperatures. In such a model, the solar wind is accelerated by the combined thermal pressures of protons and electrons,but it is mainly the difference in the proton pressure that leads to the difference in the solar wind speed. The proton pressure is the highest in the fastest CH wind, with a high initial proton temperature that decreases slowly. It is lower in the relatively slow LMBL wind, and the lowest in the slowest streamer wind. The proton temperature is quadratically correlated with the wind speed when scaled to the same distance. In contrast, the electron temperature shows no significant differences for different wind types or wind speeds, indicating more similar contributions from the electron pressure. The model gives reasonable locations for the sonic critical point, which is on average at 3.6-7.3 solar radii and can also extend to large distances when the proton temperature is extremely low, as in the LMBL wind. In addition to the thermal pressure, we raise the possibility that Alfvén waves may contribute to the solar wind acceleration, especially for the fast CH wind.

We introduce the use of Crystal Graph Convolutional Neural Networks (CGCNN), Fully Connected Neural Networks (FCNN) and XGBoost to predict thermoelectric properties. The dataset for the CGCNN is independent of Density Functional Theory (DFT) and only relies on the crystal and atomic information, while that for the FCNN is based on a rich attribute list mined from Materialsproject.org. The results show that the optimized FCNN is three layer deep and is able to predict the scattering-time independent thermoelectric powerfactor much better than the CGCNN (or XGBoost), suggesting that bonding and density of states descriptors informed from materials science knowledge obtained partially from DFT are vital to predict functional properties.

We investigate the interaction-induced resistivity of ultracold fermions in a three-dimensional optical lattice. In situ observations of transport dynamics enable the determination of real and imaginary resistivity. In the strongly interacting metallic regime, we observe a striking saturation of the current-dissipation rate towards a value that is independent of the interaction strength. This phenomenon is quantitatively captured by a dissipation model that uses a renormalized two-body scattering matrix. We further measure the temperature dependence of resistivity in the strongly interacting limit and discuss the predicted asymptotic high-temperature behavior. Our results provide a clear microscopic understanding of bounded resistivity of low-density metals, thus providing a useful benchmark for studies of strongly correlated atomic and electronic systems.

Based on a time-dependent Ginzburg-Landau system of equations and finite element modeling, we present novel results related with the physics of phase-slippage in superconducting wires surrounded by a non-superconductive environment. These results are obtained within our previously reported approach related to superconducting rings and superconductive gravitational wave detector transducers. It is shown that the phase-slip centers (PSCs) can be effective in originating not only positive but also negative thermal fluxes. With an appropriate design utilizing thermal diodes, PSCs can serve as cryocooling engines. Operating at Tsim 1 K cryostat cold-finger, they can achieve sub-Kelvin temperatures without using ^3He.

We employ a descriptor based machine-learning approach to assess the effect of chemical alloying on formation-enthalpy of rare-earth intermetallics. Application of machine-learning approaches in rare-earth intermetallic design have been sparse due to limited availability of reliable datasets. In this work, we developed an `in-house' rare-earth database with more than 600+ compounds, each entry was populated with formation enthalpy and related atomic features using high-throughput density-functional theory (DFT). The SISSO (sure independence screening and sparsifying operator) based machine-learning method with meaningful atomic features was used for training and testing the formation enthalpies of rare earth compounds. The complex lattice function coupled with the machine-learning model was used to explore the effect of transition metal alloying on the energy stability of Ce based cubic Laves phases (MgCu_{2} type). The SISSO predictions show good agreement with high-fidelity DFT calculations and X-ray powder diffraction measurements. Our study provides quantitative guidance for compositional considerations within a machine-learning model and discovering new metastable materials. The electronic-structure of Ce-Fe-Cu based compound was also analyzed in-depth to understand the electronic origin of phase stability. The interpretable analytical models in combination with density-functional theory and experiments provide a fast and reliable design guide for discovering technologically useful materials.

Accurate knowledge of the properties of hydrogen at high compression is crucial for astrophysics (e.g. planetary and stellar interiors, brown dwarfs, atmosphere of compact stars) and laboratory experiments, including inertial confinement fusion. There exists experimental data for the equation of state, conductivity, and Thomson scattering spectra. However, the analysis of the measurements at extreme pressures and temperatures typically involves additional model assumptions, which makes it difficult to assess the accuracy of the experimental data. rigorously. On the other hand, theory and modeling have produced extensive collections of data. They originate from a very large variety of models and simulations including path integral Monte Carlo (PIMC) simulations, density functional theory (DFT), chemical models, machine-learned models, and combinations thereof. At the same time, each of these methods has fundamental limitations (fermion sign problem in PIMC, approximate exchange-correlation functionals of DFT, inconsistent interaction energy contributions in chemical models, etc.), so for some parameter ranges accurate predictions are difficult. Recently, a number of breakthroughs in first principle PIMC and DFT simulations were achieved which are discussed in this review. Here we use these results to benchmark different simulation methods. We present an update of the hydrogen phase diagram at high pressures, the expected phase transitions, and thermodynamic properties including the equation of state and momentum distribution. Furthermore, we discuss available dynamic results for warm dense hydrogen, including the conductivity, dynamic structure factor, plasmon dispersion, imaginary-time structure, and density response functions. We conclude by outlining strategies to combine different simulations to achieve accurate theoretical predictions.

A tight and consistent link between resolutions is crucial to further expand the impact of multiscale modeling for complex materials. We herein tackle the generation of condensed molecular structures as a refinement -- backmapping -- of a coarse-grained structure. Traditional schemes start from a rough coarse-to-fine mapping and perform further energy minimization and molecular dynamics simulations to equilibrate the system. In this study we introduce DeepBackmap: A deep neural network based approach to directly predict equilibrated molecular structures for condensed-phase systems. We use generative adversarial networks to learn the Boltzmann distribution from training data and realize reverse mapping by using the coarse-grained structure as a conditional input. We apply our method to a challenging condensed-phase polymeric system. We observe that the model trained in a melt has remarkable transferability to the crystalline phase. The combination of data-driven and physics-based aspects of our architecture help reach temperature transferability with only limited training data.

The concentration of radon in the air has been measured in the 700 m-deep Yangyang underground laboratory between October 2004 and May 2022. The average concentrations in two experimental areas, called A6 and A5, were measured to be 53.4pm0.2 Bq/m3 and 33.5pm0.1 Bq/m3, respectively. The lower value in the A5 area reflects the presence of better temperature control and ventilation. The radon concentrations sampled within the two A5 experimental rooms' air are found to be correlated to the local surface temperature outside of the rooms, with correlation coefficients r = 0.22 and r = 0.70. Therefore, the radon concentrations display a seasonal variation, because the local temperature driven by the overground season influences air ventilation in the experimental areas. A fit on the annual residual concentrations finds that the amplitude occurs each year on August, 31pm6 days.

The o1-Like LLMs are transforming AI by simulating human cognitive processes, but their performance in multilingual machine translation (MMT) remains underexplored. This study examines: (1) how o1-Like LLMs perform in MMT tasks and (2) what factors influence their translation quality. We evaluate multiple o1-Like LLMs and compare them with traditional models like ChatGPT and GPT-4o. Results show that o1-Like LLMs establish new multilingual translation benchmarks, with DeepSeek-R1 surpassing GPT-4o in contextless tasks. They demonstrate strengths in historical and cultural translation but exhibit a tendency for rambling issues in Chinese-centric outputs. Further analysis reveals three key insights: (1) High inference costs and slower processing speeds make complex translation tasks more resource-intensive. (2) Translation quality improves with model size, enhancing commonsense reasoning and cultural translation. (3) The temperature parameter significantly impacts output quality-lower temperatures yield more stable and accurate translations, while higher temperatures reduce coherence and precision.

Thermodynamic properties of liquid water as well as hexagonal (Ih) and cubic (Ic) ice are predicted based on density functional theory at the hybrid-functional level, rigorously taking into account quantum nuclear motion, anharmonic fluctuations and proton disorder. This is made possible by combining advanced free energy methods and state-of-the-art machine learning techniques. The ab initio description leads to structural properties in excellent agreement with experiments, and reliable estimates of the melting points of light and heavy water. We observe that nuclear quantum effects contribute a crucial 0.2 meV/H_2O to the stability of ice Ih, making it more stable than ice Ic. Our computational approach is general and transferable, providing a comprehensive framework for quantitative predictions of ab initio thermodynamic properties using machine learning potentials as an intermediate step.

The application of superconducting materials is becoming more and more widespread. Traditionally, the discovery of new superconducting materials relies on the experience of experts and a large number of "trial and error" experiments, which not only increases the cost of experiments but also prolongs the period of discovering new superconducting materials. In recent years, machine learning has been increasingly applied to materials science. Based on this, this manuscript proposes the use of XGBoost model to identify superconductors; the first application of deep forest model to predict the critical temperature of superconductors; the first application of deep forest to predict the band gap of materials; and application of a new sub-network model to predict the Fermi energy level of materials. Compared with our known similar literature, all the above algorithms reach state-of-the-art. Finally, this manuscript uses the above models to search the COD public dataset and identify 50 candidate superconducting materials with possible critical temperature greater than 90 K.

As deep neural networks become adopted in high-stakes domains, it is crucial to be able to identify when inference inputs are Out-of-Distribution (OOD) so that users can be alerted of likely drops in performance and calibration despite high confidence. Among many others, existing methods use the following two scores to do so without training on any apriori OOD examples: a learned temperature and an energy score. In this paper we introduce Ablated Learned Temperature Energy (or "AbeT" for short), a method which combines these prior methods in novel ways with effective modifications. Due to these contributions, AbeT lowers the False Positive Rate at 95% True Positive Rate (FPR@95) by 35.39% in classification (averaged across all ID and OOD datasets measured) compared to state of the art without training networks in multiple stages or requiring hyperparameters or test-time backward passes. We additionally provide empirical insights as to how our model learns to distinguish between In-Distribution (ID) and OOD samples while only being explicitly trained on ID samples via exposure to misclassified ID examples at training time. Lastly, we show the efficacy of our method in identifying predicted bounding boxes and pixels corresponding to OOD objects in object detection and semantic segmentation, respectively - with an AUROC increase of 5.15% in object detection and both a decrease in FPR@95 of 41.48% and an increase in AUPRC of 34.20% on average in semantic segmentation compared to previous state of the art.