Atomic Forces

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

No benchmark for this task in the archive.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

1 dataset whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

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Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

11 shown of 11 papers with code (28 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Learning Local Equivariant Representations for Large-Scale Atomistic Dynamics
  11 Apr 2022 3 repositories listed
  This work introduces Allegro, a strictly local equivariant deep learning interatomic potential that simultaneously exhibits excellent accuracy and scalability of parallel computation.
• GotenNet: Rethinking Efficient 3D Equivariant Graph Neural Networks
  24 Apr 2025 2 repositories listed
  Understanding complex three-dimensional (3D) structures of graphs is essential for accurately modeling various properties, yet many existing approaches struggle with fully capturing the intricate spatial relationships…
• Learning atomic forces from uncertainty-calibrated adversarial attacks
  25 Feb 2025 1 repository listed
  By performing geometry optimization for calibrated uncertainty, we reach adversarial structures with the user-assigned target MLIP prediction error.
• MatSciML: A Broad, Multi-Task Benchmark for Solid-State Materials Modeling
  12 Sep 2023 1 repository listed
  We propose MatSci ML, a novel benchmark for modeling MATerials SCIence using Machine Learning (MatSci ML) methods focused on solid-state materials with periodic crystal structures.
• QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 Molecules
  15 Jun 2023 1 repository listed Syntology ran 4 of 4 samples · 0 unverified · 4 pointer-only (licence)
  Supervised machine learning approaches have been increasingly used in accelerating electronic structure prediction as surrogates of first-principle computational methods, such as density functional theory (DFT).
• CHGNet: Pretrained universal neural network potential for charge-informed atomistic modeling
  28 Feb 2023 1 repository listed
  The simulation of large-scale systems with complex electron interactions remains one of the greatest challenges for the atomistic modeling of materials.
• Transfer learning for chemically accurate interatomic neural network potentials
  7 Dec 2022 1 repository listed
  This work studies the capability of transfer learning, in particular discriminative fine-tuning, for efficiently generating chemically accurate interatomic neural network potentials on organic molecules from the MD17…
• Antibody-Antigen Docking and Design via Hierarchical Equivariant Refinement
  14 Jul 2022 1 repository listed Syntology ran 0 of 5 samples · 5 unverified
  The binding affinity is governed by the 3D binding interface where antibody residues (paratope) closely interact with antigen residues (epitope).
• Neural Network Based in Silico Simulation of Combustion Reactions
  27 Nov 2019 1 repository listed
  Through further development, the algorithms in this study can be used to explore and discovery reaction mechanisms of many complex reaction systems, such as combustion, synthesis, and heterogeneous catalysis without any…
• sGDML: Constructing Accurate and Data Efficient Molecular Force Fields Using Machine Learning
  1 Jul 2019 1 repository listed
  We present an optimized implementation of the recently proposed symmetric gradient domain machine learning (sGDML) model.
• Machine Learning of Accurate Energy-conserving Molecular Force Fields
  5 May 2017 1 repository listed
  Using conservation of energy—a fundamental property of closed classical and quantum mechanical systems—we develop an efficient gradient-domain machine learning (GDML) approach to construct accurate molecular force…

Syntology lines on 2 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.