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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 1–25 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
Neural Network Based in Silico Simulation of Combustion ReactionsCode1
Antibody-Antigen Docking and Design via Hierarchical Equivariant RefinementCode1
Ensemble Knowledge Distillation for Machine Learning Interatomic Potentials—0
Fast Neural Network Approach for Direct Covariant Forces Prediction in Complex Multi-Element Extended Systems—0
ForceNet: A Graph Neural Network for Large-Scale Quantum Chemistry Simulation—0
ForceNet: A Graph Neural Network for Large-Scale Quantum Calculations—0
Graph-Convolutional Deep Learning to Identify Optimized Molecular Configurations—0
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
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
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