Daily Papers

Computational catalysis is playing an increasingly significant role in the design of catalysts across a wide range of applications. A common task for many computational methods is the need to accurately compute the minimum binding energy - the adsorption energy - for an adsorbate and a catalyst surface of interest. Traditionally, the identification of low energy adsorbate-surface configurations relies on heuristic methods and researcher intuition. As the desire to perform high-throughput screening increases, it becomes challenging to use heuristics and intuition alone. In this paper, we demonstrate machine learning potentials can be leveraged to identify low energy adsorbate-surface configurations more accurately and efficiently. Our algorithm provides a spectrum of trade-offs between accuracy and efficiency, with one balanced option finding the lowest energy configuration, within a 0.1 eV threshold, 86.33% of the time, while achieving a 1331x speedup in computation. To standardize benchmarking, we introduce the Open Catalyst Dense dataset containing nearly 1,000 diverse surfaces and 85,658 unique configurations.

In this paper, a mathematical model is developed to describe the evolution of the concentration of compounds through a gas chromatography column. The model couples mass balances and kinetic equations for all components. Both single and multiple-component cases are considered with constant or variable velocity. Non-dimensionalisation indicates the small effect of diffusion. The system where diffusion is neglected is analysed using Laplace transforms. In the multiple-component case, it is demonstrated that the competition between the compounds is negligible and the equations may be decoupled. This reduces the problem to solving a single integral equation to determine the concentration profile for all components (since they are scaled versions of each other). For a given analyte, we then only two parameters need to be fitted to the data. To verify this approach, the full governing equations are also solved numerically using the finite difference method and a global adaptive quadrature method to integrate the Laplace transformation. Comparison with the Laplace solution verifies the high degree of accuracy of the simpler Laplace form. The Laplace solution is then verified against experimental data from BTEX chromatography. This novel method, which involves solving a single equation and fitting parameters in pairs for individual components, is highly efficient. It is significantly faster and simpler than the full numerical solution and avoids the computationally expensive methods that would normally be used to fit all curves at the same time.

Haptic displays play an important role in enhancing the sense of presence in VR and telepresence. Displaying the tactile properties of fabrics has potential in the fashion industry, but there are difficulties in dynamically displaying different types of tactile sensations while maintaining their flexible properties. The vibrotactile stimulation of fabrics is an important element in the tactile properties of fabrics, as it greatly affects the way a garment feels when rubbed against the skin. To dynamically change the vibrotactile stimuli, many studies have used mechanical actuators. However, when combined with fabric, the soft properties of the fabric are compromised by the stiffness of the actuator. In addition, because the vibration generated by such actuators is applied to a single point, it is not possible to provide a uniform tactile sensation over the entire surface of the fabric, resulting in an uneven tactile sensation. In this study, we propose a Pinching Tactile Display: a conductive cloth that changes the tactile sensation by controlling electrostatic adsorption. By controlling the voltage and frequency applied to the conductive cloth, different tactile sensations can be dynamically generated. This makes it possible to create a tactile device in which tactile sensations are applied to the entire fabric while maintaining the thin and soft characteristics of the fabric. As a result, users could experiment with tactile sensations by picking up and rubbing the fabric in the same way they normally touch it. This mechanism has the potential for dynamic tactile transformation of soft materials.

We study the effects of H2O on CO2 adsorption in an amine-appended variant of the metal-organic framework Mg2(dobpdc), which is known to exhibit chaining behavior that presents in a step-shaped adsorption isotherm. We first show how the presence of different levels of local H2O affects this chaining behavior and the energetics of CO2 adsorption, based on a series of ab initio calculations, giving insight into the atomic-scale environment. In particular, we predict a novel adsorbed configuration, in which H2O and CO2 intertwine to make a braided chain down the MOF pore. We then show how an existing lattice model can be adapted to incorporate the effect of water, and predict the CO2 isotherms for the various water levels, observing a sharp shift the uptake at low partial pressures. In addition to the physical further work on this and related materials.

This paper reports on a new and swift hydrothermal chemical route to prepare titanate nanostructures (TNS) avoiding the use of crystalline TiO2 as starting material. The synthesis approach uses a commercial solution of TiCl3 as titanium source to prepare an amorphous precursor, circumventing the use of hazardous chemical compounds. The influence of the reaction temperature and dwell autoclave time on the structure and morphology of the synthesised materials was studied. Homogeneous titanate nanotubes with a high length/diameter aspect ratio were synthesised at 160^{\circ}C and 24 h. A band gap of 3.06\pm0.03 eV was determined for the TNS samples prepared in these experimental conditions. This value is red shifted by 0.14 eV compared to the band gap value usually reported for the TiO2 anatase. Moreover, such samples show better adsorption capacity and photocatalytic performance on the dye rhodamine 6G (R6G) photodegradation process than TiO2 nanoparticles. A 98% reduction of the R6G concentration was achieved after 45 minutes of irradiation of a 10 ppm dye aqueous solution and 1 g/L of TNS catalyst.

Disorder and defective coordination in the catalytic plane are crucial for enhancing the Hydrogen Evolution Reaction (HER) on two-dimensional catalysts. Amorphous materials are disordered, making them catalytically adaptive for many reactions. In this work, the HER capabilities of Monolayer Amorphous Carbon (MAC) were studied in comparison with crystalline carbon derivatives, such as pristine graphene (GE) and graphyne derivatives. MAC generated from melt-quench simulations revealed a diverse framework of predominantly sp2 and sp3 carbons with numerous 5-, 6-, and 7-membered rings. Density Functional Theory (DFT) calculations investigated free-energy variations in hydrogen adsorption for each material. According to Sabatier's principle, optimum activity is achieved when the Gibbs free energy (Delta GH) change approaches zero. Crystalline carbon materials possess limited active sites, with beta-graphyne showing the best Delta GH value of +0.34 eV. The adsorption study for MAC was conducted in 30 distinct local environments, where core structural properties were analyzed against varying radii. Calculations showed a Delta GH distribution for MAC ranging from -0.02 eV to +1.35 eV. To evaluate activity across the entire MAC surface, a MACE MLIP foundation model was finetuned, achieving optimal energy and force fitting of 1.67 meV/atom and 29.15 meV/A, respectively. The MLIP predicted Delta GH values from -0.91 eV to +1.70 eV, with approximately 15% of sites exhibiting values below +0.25 eV. Feature analysis revealed that 7-membered rings, curvature, and ripple height enhance HER activity. Our findings suggest that, with careful optimization of local features, MAC can be tuned to compete with noble metal catalysts.

The use of chemical warfare agents (CWAs) in modern warfare cannot be disregarded due to their ease of use and potential for large-scale incapacitation. An effective countermeasure involves the physical adsorption of these agents, preventing their entry through the respiratory tract by non-specific adsorption. In this study, we investigate the physical interaction between potential adsorbents and model gases mimicking CWAs, thereby identifying sufficient conditions for higher physical adsorption performance. Our findings reveal that the physical adsorption capacity is highly sensitive to the surface properties of the adsorbents, with uniform development of micropores, rather than solely high surface area, emerging as a critical factor. Additionally, we identified the potential of porous organic polymers as promising alternatives to conventional activated carbon-based adsorbents. Through a facile introduction of polar sulfone functional groups on the polymer surface, we demonstrated that these polar surface polymers exhibit physical adsorption capabilities for formaldehyde under ambient conditions comparable to high-performance activated carbons. Notably, the superior activated carbon possessed a high BET surface area of 2400 m^2/g and an exceptionally uniform micropore structure with an average pore size of approximately 11 Angstroms. This research paves the way for designing adsorbents with high physical adsorption capacities tailored for CWAs protection, offering a significant advancement in developing next-generation protective materials.

Single-atom catalysts (SACs) have emerged as frontiers for catalyzing chemical reactions, yet the diverse combinations of active elements and support materials, the nature of coordination environments, elude traditional methodologies in searching optimal SAC systems with superior catalytic performance. Herein, by integrating multi-branch Convolutional Neural Network (CNN) analysis models to hybrid descriptor based activity volcano plot, 2D SAC system composed of diverse metallic single atoms anchored on six type of 2D supports, including graphitic carbon nitride, nitrogen-doped graphene, graphene with dual-vacancy, black phosphorous, boron nitride, and C2N, are screened for efficient CO2RR. Starting from establishing a correlation map between the adsorption energies of intermediates and diverse electronic and elementary descriptors, sole singular descriptor lost magic to predict catalytic activity. Deep learning method utilizing multi-branch CNN model therefore was employed, using 2D electronic density of states as input to predict adsorption energies. Hybrid-descriptor enveloping both C- and O-types of CO2RR intermediates was introduced to construct volcano plots and limiting potential periodic table, aiming for intuitive screening of catalyst candidates for efficient CO2 reduction to CH4. The eDOS occlusion experiments were performed to unravel individual orbital contribution to adsorption energy. To explore the electronic scale principle governing practical engineering catalytic CO2RR activity, orbitalwise eDOS shifting experiments based on CNN model were employed. The study involves examining the adsorption energy and, consequently, catalytic activities while varying supported single atoms. This work offers a tangible framework to inform both theoretical screening and experimental synthesis, thereby paving the way for systematically designing efficient SACs.

Most recent exciting experimental advances introduced buckled and flat borophene nanomembranes as new members to the advancing family of two-dimensional (2D) materials. Borophene, is the boron atom analogue of graphene with interesting properties suitable for a wide variety of applications. In this investigation, we conducted extensive first-principles density functional theory simulations to explore the application of four different flat borophene films as anode materials for Al, Mg, Na or Li-ion batteries. In our modelling, first the strongest binding sites were predicted and next we gradually increased the adatoms coverage until the maximum capacity was reached. Bader charge analysis was employed to evaluate the charge transfer between the adatoms and the borophene films. Nudged elastic band method was also utilized to probe the ions diffusions. We calculated the average atom adsorption energies and open-circuit voltage profiles as a function of adatoms coverage. Our findings propose the flat borophene films as electrically conductive and thermally stable anode materials with ultra high capacities of 2480 mAh/g, 1640 mAh/g and 2040 mAh/g for Mg, Na or Li-ion batteries, respectively, which distinctly outperform not only the buckled borophene but also all other 2D materials. Our study may provide useful viewpoint with respect to the possible application of flat borophene films for the design of high capacity and light weight advanced rechargeable ion batteries.

New methods for carbon dioxide removal are urgently needed to combat global climate change. Direct air capture (DAC) is an emerging technology to capture carbon dioxide directly from ambient air. Metal-organic frameworks (MOFs) have been widely studied as potentially customizable adsorbents for DAC. However, discovering promising MOF sorbents for DAC is challenging because of the vast chemical space to explore and the need to understand materials as functions of humidity and temperature. We explore a computational approach benefiting from recent innovations in machine learning (ML) and present a dataset named Open DAC 2023 (ODAC23) consisting of more than 38M density functional theory (DFT) calculations on more than 8,400 MOF materials containing adsorbed CO_2 and/or H_2O. ODAC23 is by far the largest dataset of MOF adsorption calculations at the DFT level of accuracy currently available. In addition to probing properties of adsorbed molecules, the dataset is a rich source of information on structural relaxation of MOFs, which will be useful in many contexts beyond specific applications for DAC. A large number of MOFs with promising properties for DAC are identified directly in ODAC23. We also trained state-of-the-art ML models on this dataset to approximate calculations at the DFT level. This open-source dataset and our initial ML models will provide an important baseline for future efforts to identify MOFs for a wide range of applications, including DAC.

The development of machine learning models for electrocatalysts requires a broad set of training data to enable their use across a wide variety of materials. One class of materials that currently lacks sufficient training data is oxides, which are critical for the development of OER catalysts. To address this, we developed the OC22 dataset, consisting of 62,331 DFT relaxations (~9,854,504 single point calculations) across a range of oxide materials, coverages, and adsorbates. We define generalized total energy tasks that enable property prediction beyond adsorption energies; we test baseline performance of several graph neural networks; and we provide pre-defined dataset splits to establish clear benchmarks for future efforts. In the most general task, GemNet-OC sees a ~36% improvement in energy predictions when combining the chemically dissimilar OC20 and OC22 datasets via fine-tuning. Similarly, we achieved a ~19% improvement in total energy predictions on OC20 and a ~9% improvement in force predictions in OC22 when using joint training. We demonstrate the practical utility of a top performing model by capturing literature adsorption energies and important OER scaling relationships. We expect OC22 to provide an important benchmark for models seeking to incorporate intricate long-range electrostatic and magnetic interactions in oxide surfaces. Dataset and baseline models are open sourced, and a public leaderboard is available to encourage continued community developments on the total energy tasks and data.

Artificial intelligence is reshaping scientific exploration, but most methods automate procedural tasks without engaging in scientific reasoning, limiting autonomy in discovery. We introduce Materials Agents for Simulation and Theory in Electronic-structure Reasoning (MASTER), an active learning framework where large language models autonomously design, execute, and interpret atomistic simulations. In MASTER, a multimodal system translates natural language into density functional theory workflows, while higher-level reasoning agents guide discovery through a hierarchy of strategies, including a single agent baseline and three multi-agent approaches: peer review, triage-ranking, and triage-forms. Across two chemical applications, CO adsorption on Cu-surface transition metal (M) adatoms and on M-N-C catalysts, reasoning-driven exploration reduces required atomistic simulations by up to 90% relative to trial-and-error selection. Reasoning trajectories reveal chemically grounded decisions that cannot be explained by stochastic sampling or semantic bias. Altogether, multi-agent collaboration accelerates materials discovery and marks a new paradigm for autonomous scientific exploration.

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.

A central challenge of the clean energy transition is the development of catalysts for low-emissions technologies. Recent advances in Machine Learning for quantum chemistry drastically accelerate the computation of catalytic activity descriptors such as adsorption energies. Here we introduce AdsorbRL, a Deep Reinforcement Learning agent aiming to identify potential catalysts given a multi-objective binding energy target, trained using offline learning on the Open Catalyst 2020 and Materials Project data sets. We experiment with Deep Q-Network agents to traverse the space of all ~160,000 possible unary, binary and ternary compounds of 55 chemical elements, with very sparse rewards based on adsorption energy known for only between 2,000 and 3,000 catalysts per adsorbate. To constrain the actions space, we introduce Random Edge Traversal and train a single-objective DQN agent on the known states subgraph, which we find strengthens target binding energy by an average of 4.1 eV. We extend this approach to multi-objective, goal-conditioned learning, and train a DQN agent to identify materials with the highest (respectively lowest) adsorption energies for multiple simultaneous target adsorbates. We experiment with Objective Sub-Sampling, a novel training scheme aimed at encouraging exploration in the multi-objective setup, and demonstrate simultaneous adsorption energy improvement across all target adsorbates, by an average of 0.8 eV. Overall, our results suggest strong potential for Deep Reinforcement Learning applied to the inverse catalysts design problem.

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.

Recently, the advent of pre-trained large-scale language models (LLMs) like ChatGPT and GPT-4 have significantly advanced the machine's natural language understanding capabilities. This breakthrough has allowed us to seamlessly integrate these open-source LLMs into a unified robot simulator environment to help robots accurately understand and execute human natural language instructions. To this end, in this work, we introduce a realistic robotic manipulation simulator and build a Robotic Manipulation with Progressive Reasoning Tasks (RM-PRT) benchmark on this basis. Specifically, the RM-PRT benchmark builds a new high-fidelity digital twin scene based on Unreal Engine 5, which includes 782 categories, 2023 objects, and 15K natural language instructions generated by ChatGPT for a detailed evaluation of robot manipulation. We propose a general pipeline for the RM-PRT benchmark that takes as input multimodal prompts containing natural language instructions and automatically outputs actions containing the movement and position transitions. We set four natural language understanding tasks with progressive reasoning levels and evaluate the robot's ability to understand natural language instructions in two modes of adsorption and grasping. In addition, we also conduct a comprehensive analysis and comparison of the differences and advantages of 10 different LLMs in instruction understanding and generation quality. We hope the new simulator and benchmark will facilitate future research on language-guided robotic manipulation. Project website: https://necolizer.github.io/RM-PRT/ .

This paper introduces Galactic Bioware's Life Support System, a semi-closed-circuit breathing apparatus designed for integration into a positive-pressure firefighting suit and governed by an AI control system. The breathing loop incorporates a soda lime CO2 scrubber, a silica gel dehumidifier, and pure O2 replenishment with finite consumables. One-way exhaust valves maintain positive pressure while creating a semi-closed system in which outward venting gradually depletes the gas inventory. Part I develops the physicochemical foundations from first principles, including state-consistent thermochemistry, stoichiometric capacity limits, adsorption isotherms, and oxygen-management constraints arising from both fire safety and toxicity. Part II introduces an AI control architecture that fuses three sensor tiers, external environmental sensing, internal suit atmosphere sensing (with triple-redundant O2 cells and median voting), and firefighter biometrics. The controller combines receding-horizon model-predictive control (MPC) with a learned metabolic model and a reinforcement learning (RL) policy advisor, with all candidate actuator commands passing through a final control-barrier-function safety filter before reaching the hardware. This architecture is intended to optimize performance under unknown mission duration and exertion profiles. In this paper we introduce an 18-state, 3-control nonlinear state-space formulation using only sensors viable in structural firefighting, with triple-redundant O2 sensing and median voting. Finally, we introduce an MPC framework with a dynamic resource scarcity multiplier, an RL policy advisor for warm-starting, and a final control-barrier-function safety filter through which all actuator commands must pass, demonstrating 18-34% endurance improvement in simulation over PID baselines while maintaining tighter physiological and fire-safety margins.

Deep generative models have shown promise for modeling metal-organic frameworks (MOFs), but existing approaches (1) rely on coarse-grained representations that assume fixed bond lengths and angles, and (2) neglect the MOF-adsorbate interactions, which are critical for downstream applications. We introduce AtomMOF, a scalable flow-based model built on an all-atom Diffusion Transformer that maps 2D molecular graphs of building blocks and adsorbates directly to equilibrium 3D structures without imposing structural constraints. We further present scaling laws for porous crystal generation, indicating predictable performance gains with increased model capacity, and introduce Feynman-Kac steering guided by machine-learned interatomic potentials to improve geometric validity and sampling stability. On the (MOF-only) BW dataset, AtomMOF increases the match rate by 35.00% and reduces RMSD by 32.64%. On the ODAC25 dataset (MOF-adsorbate), AtomMOF is substantially more sample-efficient than grand canonical Monte Carlo in recovering adsorption configurations and can identify candidates with lower adsorption energies than the reference dataset. Code is available at https://github.com/nayoung10/AtomMOF.

Identifying useful sorbent materials for direct air capture (DAC) from humid air remains a challenge. We present the Open DAC 2025 (ODAC25) dataset, a significant expansion and improvement upon ODAC23 (Sriram et al., ACS Central Science, 10 (2024) 923), comprising nearly 70 million DFT single-point calculations for CO_2, H_2O, N_2, and O_2 adsorption in 15,000 MOFs. ODAC25 introduces chemical and configurational diversity through functionalized MOFs, high-energy GCMC-derived placements, and synthetically generated frameworks. ODAC25 also significantly improves upon the accuracy of DFT calculations and the treatment of flexible MOFs in ODAC23. Along with the dataset, we release new state-of-the-art machine-learned interatomic potentials trained on ODAC25 and evaluate them on adsorption energy and Henry's law coefficient predictions.

We present MOFA, an open-source generative AI (GenAI) plus simulation workflow for high-throughput generation of metal-organic frameworks (MOFs) on large-scale high-performance computing (HPC) systems. MOFA addresses key challenges in integrating GPU-accelerated computing for GPU-intensive GenAI tasks, including distributed training and inference, alongside CPU- and GPU-optimized tasks for screening and filtering AI-generated MOFs using molecular dynamics, density functional theory, and Monte Carlo simulations. These heterogeneous tasks are unified within an online learning framework that optimizes the utilization of available CPU and GPU resources across HPC systems. Performance metrics from a 450-node (14,400 AMD Zen 3 CPUs + 1800 NVIDIA A100 GPUs) supercomputer run demonstrate that MOFA achieves high-throughput generation of novel MOF structures, with CO_2 adsorption capacities ranking among the top 10 in the hypothetical MOF (hMOF) dataset. Furthermore, the production of high-quality MOFs exhibits a linear relationship with the number of nodes utilized. The modular architecture of MOFA will facilitate its integration into other scientific applications that dynamically combine GenAI with large-scale simulations.

Equivariant Transformers such as Equiformer have demonstrated the efficacy of applying Transformers to the domain of 3D atomistic systems. However, they are still limited to small degrees of equivariant representations due to their computational complexity. In this paper, we investigate whether these architectures can scale well to higher degrees. Starting from Equiformer, we first replace SO(3) convolutions with eSCN convolutions to efficiently incorporate higher-degree tensors. Then, to better leverage the power of higher degrees, we propose three architectural improvements -- attention re-normalization, separable S^2 activation and separable layer normalization. Putting this all together, we propose EquiformerV2, which outperforms previous state-of-the-art methods on the large-scale OC20 dataset by up to 12% on forces, 4% on energies, offers better speed-accuracy trade-offs, and 2times reduction in DFT calculations needed for computing adsorption energies.