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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 21–28 of 28 papers

TitleStatusHype
Simple and efficient algorithms for training machine learning potentials to force data—0
Symmetry-invariant quantum machine learning force fields—0
Machine Learning of Accurate Energy-conserving Molecular Force FieldsCode0
MatSciML: A Broad, Multi-Task Benchmark for Solid-State Materials ModelingCode0
sGDML: Constructing Accurate and Data Efficient Molecular Force Fields Using Machine LearningCode0
Learning atomic forces from uncertainty-calibrated adversarial attacksCode0
Transfer learning for chemically accurate interatomic neural network potentialsCode0
QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 MoleculesCode0
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