Datasets:
| paper,group,title,domain,venue,citations,lab,original_paper,follow_up_direction,task_spec,paper_pdf,paper_md | |
| P1,A,Hierarchical modeling of molecular energies using a deep neural network,Computational chemistry,J. Chem. Phys.,428,LANL,https://arxiv.org/abs/1710.00017,Use HIP-NN's hierarchy-breakdown signal to identify failure motifs and calibrate uncertainty estimates that transfer across conformations.,benchmark/paper1/task_spec.md,benchmark/paper1/paper.pdf,benchmark/paper1/paper.md | |
| P2,A,Extending the accuracy of the SNAP interatomic potential form,Materials science,J. Chem. Phys.,311,SNL,https://arxiv.org/abs/1711.11131,"Replace SNAP bispectrum components with a learned descriptor and test its accuracy, data efficiency, and cross-material transfer.",benchmark/paper2/task_spec.md,benchmark/paper2/paper.pdf,benchmark/paper2/paper.md | |
| P3,A,Convolutional Dictionary Learning: A Comparative Review and New Algorithms,Computational imaging,IEEE TCI,282,LANL,https://arxiv.org/abs/1709.02893,Unify CDL dictionary-update algorithms within one multichannel derivation framework and use it to reveal a new method and equivalence.,benchmark/paper3/task_spec.md,benchmark/paper3/paper.pdf,benchmark/paper3/paper.md | |
| P4,A,A Kriging-based approach to autonomous experimentation with applications to X-ray scattering,Scientific ML,Sci. Rep.,136,LBNL,https://doi.org/10.1038/s41598-019-48114-3,Develop an acquisition strategy that reconstructs synthetic functions faster than SMART and gSMART.,benchmark/paper4/task_spec.md,benchmark/paper4/paper.pdf,benchmark/paper4/paper.md | |
| P5,A,Adaptive variational quantum dynamics simulations,Quantum,PRX Quantum,203,Ames,https://arxiv.org/abs/2011.00622,Make AVQDS compatible with fixed nearest-neighbor qubit topology while measuring the resulting accuracy and resource overhead.,benchmark/paper5/task_spec.md,benchmark/paper5/paper.pdf,benchmark/paper5/paper.md | |
| P6,A,Simulations of relativistic-quantum plasmas using real-time lattice scalar QED,Plasma physics,Phys. Rev. E,32,PPPL,https://arxiv.org/abs/1802.00524,"Stress-test variational lattice scalar QED in new valid and fundamentally invalid regimes, then diagnose and repair its failures.",benchmark/paper6/task_spec.md,benchmark/paper6/paper.pdf,benchmark/paper6/paper.md | |
| P7,A,The non-Riemannian nature of perceptual color space,Color science,PNAS,51,LANL,https://doi.org/10.1073/pnas.2119753119,Test whether perceptual diminishing returns are organized by cone channels rather than global scaling or generic anisotropy.,benchmark/paper7/task_spec.md,benchmark/paper7/paper.pdf,benchmark/paper7/paper.md | |
| P8,A,Parameterized Physics-informed Neural Networks for Parameterized PDEs,Scientific ML,ICML,113,,https://arxiv.org/abs/2408.09446,"Extend P²INN into a problem-specification encoder that generalizes jointly across coefficients, initial conditions, and boundary conditions.",benchmark/paper8/task_spec.md,benchmark/paper8/paper.pdf,benchmark/paper8/paper.md | |
| P9,B,An efficient explicit implementation of a near-optimal quantum algorithm for simulating linear dissipative differential equations,Quantum,J. Comput. Phys.,6,LLNL,https://arxiv.org/abs/2501.11146,Improve the mathematical LCHS algorithm by substituting a kernel with better asymptotic resource scaling.,benchmark/paper9/task_spec.md,benchmark/paper9/paper.pdf,benchmark/paper9/paper.md | |
| P10,B,Efficient Berry Phase Calculation via Adaptive Variational Quantum Computing Approach,Quantum,APL Quantum,5,Ames,https://arxiv.org/abs/2506.19150,Determine how much realistic hardware noise the AVQDS Berry-phase method can tolerate before its topological output fails.,benchmark/paper10/task_spec.md,benchmark/paper10/paper.pdf,benchmark/paper10/paper.md | |
| P11,B,Projected Hessian Learning: Fast Curvature Supervision for Accurate Machine-Learning Interatomic Potentials,Computational chemistry,Unpublished,3,LANL,https://arxiv.org/abs/2603.04523,Reduce the stochastic-probe requirement of Projected Hessian Learning without sacrificing estimator or downstream model quality.,benchmark/paper11/task_spec.md,benchmark/paper11/paper.pdf,benchmark/paper11/paper.md | |
| P12,B,gp2Scale: Compactly-Supported Non-Stationary Kernels and Distributed Computing for Exact Gaussian Processes on 10 Million Data Points,Scientific ML,ICML,2,LBNL,https://arxiv.org/abs/2512.06143,Apply exact large-scale Gaussian processes to a new domain using a domain-specific kernel or sparsity construction.,benchmark/paper12/task_spec.md,benchmark/paper12/paper.pdf,benchmark/paper12/paper.md | |
| P13,B,Particle-in-cell simulations of laser crossbeam energy transfer via magnetized ion-acoustic wave,Plasma physics,Physics,2,LLNL,https://arxiv.org/abs/2511.03090,Map the boundary where Vlasov theory ceases to predict MagCBET PIC behavior and identify the first invalid assumption.,benchmark/paper13/task_spec.md,benchmark/paper13/paper.pdf,benchmark/paper13/paper.md | |
| P14,B,Simulating plasma wave propagation on a superconducting quantum chip,Quantum,Phys. Rev. App.,1,LLNL,https://arxiv.org/abs/2507.09479,Add ZZ interactions to the spin-chain model and test whether it quantitatively reproduces nonlinear plasma dynamics.,benchmark/paper14/task_spec.md,benchmark/paper14/paper.pdf,benchmark/paper14/paper.md | |
| P15,B,Beyond Classical Molecular Dynamics with Layered Interatomic Potentials,Materials science,Unpublished,0,SNL,https://www.researchsquare.com/article/rs-9077430/v1,Replace the Sommerfeld potential's heuristic sigmoid with a physically principled switching function.,benchmark/paper15/task_spec.md,benchmark/paper15/paper.pdf,benchmark/paper15/paper.md | |