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Articles Citing

Multilayer atomic cluster expansion for semilocal interactions

Graph atomic cluster expansion for foundational machine learning interatomic potentials

Recent advances in electronic structure learning

Efficient Parallelization of Message Passing Neural Network Potentials for Large-Scale Molecular Dynamics

Water Phase Diagram from a General-Purpose Atomic Cluster Expansion Potential

Reactive Machine Learning Interatomic Potentials for Chemistry and Materials Science

Comparison of DeePMD, MTP, GAP, ACE and MACE Machine‐Learned Potentials for Radiation‐Damage Simulations: A User Perspective

Efficient exploration of reaction pathways using reaction databases and active learning

Charge-constrained atomic cluster expansion

Efficient local atomic cluster expansion for BaTiO3 close to equilibrium

The design space of E(3)-equivariant atom-centred interatomic potentials

SuperSalt: equivariant neural network force fields for multicomponent molten salts system

Introduction to machine learning potentials for atomistic simulations

A practical guide to machine learning interatomic potentials – Status and future

Thermal transport properties of 2D narrow bandgap semiconductor Ca3N2, Ba3P2, and Ba3As2: Machine learning potential study

How graph neural network interatomic potentials extrapolate: Role of the message-passing algorithm

A reactive molecular dynamics model for uranium/hydrogen containing systems

Graph Atomic Cluster Expansion for Semilocal Interactions beyond Equivariant Message Passing

Cartesian atomic cluster expansion for machine learning interatomic potentials

Atomic cluster expansion interatomic potential for defects and thermodynamics of Cu–W system

To Pair or not to Pair? Machine-Learned Explicitly-Correlated Electronic Structure for NaCl in Water

Efficient Parametrization of Transferable Atomic Cluster Expansion for Water

3DReact: Geometric Deep Learning for Chemical Reactions

ACEpotentials.jl: A Julia implementation of the atomic cluster expansion

Stress and heat flux via automatic differentiation

How to validate machine-learned interatomic potentials

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