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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

TitleStatusHype
GotenNet: Rethinking Efficient 3D Equivariant Graph Neural NetworksCode2
CHGNet: Pretrained universal neural network potential for charge-informed atomistic modelingCode2
Learning Local Equivariant Representations for Large-Scale Atomistic DynamicsCode2
Neural Network Based in Silico Simulation of Combustion ReactionsCode1
Antibody-Antigen Docking and Design via Hierarchical Equivariant RefinementCode1
EL-MLFFs: Ensemble Learning of Machine Leaning Force Fields—0
EGraFFBench: Evaluation of Equivariant Graph Neural Network Force Fields for Atomistic Simulations—0
Chemistry-Inspired Diffusion with Non-Differentiable Guidance—0
ForceNet: A Graph Neural Network for Large-Scale Quantum Chemistry Simulation—0
Fast Neural Network Approach for Direct Covariant Forces Prediction in Complex Multi-Element Extended Systems—0
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