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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 11–20 of 28 papers

TitleStatusHype
Scalable Training of Trustworthy and Energy-Efficient Predictive Graph Foundation Models for Atomistic Materials Modeling: A Case Study with HydraGNN—0
EL-MLFFs: Ensemble Learning of Machine Leaning Force Fields—0
Symmetry-invariant quantum machine learning force fields—0
EGraFFBench: Evaluation of Equivariant Graph Neural Network Force Fields for Atomistic Simulations—0
MatSciML: A Broad, Multi-Task Benchmark for Solid-State Materials ModelingCode0
May the Force be with You: Unified Force-Centric Pre-Training for 3D Molecular Conformations—0
QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 MoleculesCode0
A Heterogeneous Parallel Non-von Neumann Architecture System for Accurate and Efficient Machine Learning Molecular Dynamics—0
Transfer learning for chemically accurate interatomic neural network potentialsCode0
Learning inducing points and uncertainty on molecular data by scalable variational Gaussian processes—0
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