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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
Learning inducing points and uncertainty on molecular data by scalable variational Gaussian processes—0
A Heterogeneous Parallel Non-von Neumann Architecture System for Accurate and Efficient Machine Learning Molecular Dynamics—0
Chemistry-Inspired Diffusion with Non-Differentiable Guidance—0
Constructing accurate machine-learned potentials and performing highly efficient atomistic simulations to predict structural and thermal properties—0
EGraFFBench: Evaluation of Equivariant Graph Neural Network Force Fields for Atomistic Simulations—0
EL-MLFFs: Ensemble Learning of Machine Leaning Force Fields—0
May the Force be with You: Unified Force-Centric Pre-Training for 3D Molecular Conformations—0
Nudged elastic band calculations accelerated with Gaussian process regression—0
REBIND: Enhancing ground-state molecular conformation via force-based graph rewiring—0
Scalable Training of Trustworthy and Energy-Efficient Predictive Graph Foundation Models for Atomistic Materials Modeling: A Case Study with HydraGNN—0
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