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Atomic Forces

Predicion of the atomic forces, generally calculated with a quantum mechanical code (e.g. at DFT theory).

Papers

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Showing 1–10 of 28 papers

Title	Date	Tasks	Status	Hype	Score
CHGNet: Pretrained universal neural network potential for charge-informed atomistic modeling	Feb 28, 2023	Atomic ForcesGraph Neural Network	CodeCode Available	2	5
GotenNet: Rethinking Efficient 3D Equivariant Graph Neural Networks	Apr 24, 2025	Atomic ForcesComputational Efficiency	CodeCode Available	2	5
Learning Local Equivariant Representations for Large-Scale Atomistic Dynamics	Apr 11, 2022	Atomic Forces	CodeCode Available	2	5
Neural Network Based in Silico Simulation of Combustion Reactions	Nov 27, 2019	Atomic Forces	CodeCode Available	1	5
Antibody-Antigen Docking and Design via Hierarchical Equivariant Refinement	Jul 14, 2022	Atomic ForcesDecoder	CodeCode Available	1	5
MatSciML: A Broad, Multi-Task Benchmark for Solid-State Materials Modeling	Sep 12, 2023	Atomic ForcesDiversity	CodeCode Available	0	5
QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 Molecules	Jun 15, 2023	Atomic Forces	CodeCode Available	0	5
Learning atomic forces from uncertainty-calibrated adversarial attacks	Feb 25, 2025	Active LearningAtomic Forces	CodeCode Available	0	5
Machine Learning of Accurate Energy-conserving Molecular Force Fields	May 5, 2017	Atomic ForcesBIG-bench Machine Learning	CodeCode Available	0	5
sGDML: Constructing Accurate and Data Efficient Molecular Force Fields Using Machine Learning	Jul 1, 2019	Atomic ForcesBIG-bench Machine Learning	CodeCode Available	0	5

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