MACE: Higher order equivariant message passing neural networks for fast and accurate force fields
Creating fast and accurate force fields is a long-standing challenge in computational
chemistry and materials science. Recently, Equivariant Message Passing Neural Networks …
chemistry and materials science. Recently, Equivariant Message Passing Neural Networks …
Learning local equivariant representations for large-scale atomistic dynamics
A simultaneously accurate and computationally efficient parametrization of the potential
energy surface of molecules and materials is a long-standing goal in the natural sciences …
energy surface of molecules and materials is a long-standing goal in the natural sciences …
Construction of high accuracy machine learning interatomic potential for surface/interface of nanomaterials—A review
The inherent discontinuity and unique dimensional attributes of nanomaterial surfaces and
interfaces bestow them with various exceptional properties. These properties, however, also …
interfaces bestow them with various exceptional properties. These properties, however, also …
Data generation for machine learning interatomic potentials and beyond
The field of data-driven chemistry is undergoing an evolution, driven by innovations in
machine learning models for predicting molecular properties and behavior. Recent strides in …
machine learning models for predicting molecular properties and behavior. Recent strides in …
E (3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials
Abstract This work presents Neural Equivariant Interatomic Potentials (NequIP), an E (3)-
equivariant neural network approach for learning interatomic potentials from ab-initio …
equivariant neural network approach for learning interatomic potentials from ab-initio …
DeePMD-kit v2: A software package for deep potential models
DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics
simulations using machine learning potentials known as Deep Potential (DP) models. This …
simulations using machine learning potentials known as Deep Potential (DP) models. This …
Forces are not enough: Benchmark and critical evaluation for machine learning force fields with molecular simulations
Molecular dynamics (MD) simulation techniques are widely used for various natural science
applications. Increasingly, machine learning (ML) force field (FF) models begin to replace ab …
applications. Increasingly, machine learning (ML) force field (FF) models begin to replace ab …
GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations
We present our latest advancements of machine-learned potentials (MLPs) based on the
neuroevolution potential (NEP) framework introduced in Fan et al.[Phys. Rev. B 104, 104309 …
neuroevolution potential (NEP) framework introduced in Fan et al.[Phys. Rev. B 104, 104309 …
The design space of E (3)-equivariant atom-centred interatomic potentials
Molecular dynamics simulation is an important tool in computational materials science and
chemistry, and in the past decade it has been revolutionized by machine learning. This rapid …
chemistry, and in the past decade it has been revolutionized by machine learning. This rapid …
[HTML][HTML] Evaluation of the MACE force field architecture: From medicinal chemistry to materials science
The MACE architecture represents the state of the art in the field of machine learning force
fields for a variety of in-domain, extrapolation, and low-data regime tasks. In this paper, we …
fields for a variety of in-domain, extrapolation, and low-data regime tasks. In this paper, we …