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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
Machine Learning of Accurate Energy-conserving Molecular Force FieldsCode0
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
Simple and efficient algorithms for training machine learning potentials to force data—0
Symmetry-invariant quantum machine learning force fields—0
Learning inducing points and uncertainty on molecular data by scalable variational Gaussian processes—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
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