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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 1–28 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
Antibody-Antigen Docking and Design via Hierarchical Equivariant RefinementCode1
Neural Network Based in Silico Simulation of Combustion ReactionsCode1
Ensemble Knowledge Distillation for Machine Learning Interatomic Potentials—0
Learning atomic forces from uncertainty-calibrated adversarial attacksCode0
Constructing accurate machine-learned potentials and performing highly efficient atomistic simulations to predict structural and thermal properties—0
Chemistry-Inspired Diffusion with Non-Differentiable Guidance—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
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
Graph-Convolutional Deep Learning to Identify Optimized Molecular Configurations—0
ForceNet: A Graph Neural Network for Large-Scale Quantum Calculations—0
ForceNet: A Graph Neural Network for Large-Scale Quantum Chemistry Simulation—0
Simple and efficient algorithms for training machine learning potentials to force data—0
sGDML: Constructing Accurate and Data Efficient Molecular Force Fields Using Machine LearningCode0
Fast Neural Network Approach for Direct Covariant Forces Prediction in Complex Multi-Element Extended Systems—0
Nudged elastic band calculations accelerated with Gaussian process regression—0
Machine Learning of Accurate Energy-conserving Molecular Force FieldsCode0
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