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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
Antibody-Antigen Docking and Design via Hierarchical Equivariant RefinementCode1
Neural Network Based in Silico Simulation of Combustion ReactionsCode1
Ensemble Knowledge Distillation for Machine Learning Interatomic Potentials—0
Learning atomic forces from uncertainty-calibrated adversarial attacksCode0
Constructing accurate machine-learned potentials and performing highly efficient atomistic simulations to predict structural and thermal properties—0
Chemistry-Inspired Diffusion with Non-Differentiable Guidance—0
REBIND: Enhancing ground-state molecular conformation via force-based graph rewiring—0
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