Cartesian atomic cluster expansion for machine learning interatomic potentials
B Cheng - npj Computational Materials, 2024 - nature.com
Abstract Machine learning interatomic potentials are revolutionizing large-scale, accurate
atomistic modeling in material science and chemistry. Many potentials use atomic cluster …
atomistic modeling in material science and chemistry. Many potentials use atomic cluster …
Smooth, exact rotational symmetrization for deep learning on point clouds
Point clouds are versatile representations of 3D objects and have found widespread
application in science and engineering. Many successful deep-learning models have been …
application in science and engineering. Many successful deep-learning models have been …
Symmetry-invariant quantum machine learning force fields
Machine learning techniques are essential tools to compute efficient, yet accurate, force
fields for atomistic simulations. This approach has recently been extended to incorporate …
fields for atomistic simulations. This approach has recently been extended to incorporate …
Complete and Efficient Covariants for 3D Point Configurations with Application to Learning Molecular Quantum Properties
When modeling physical properties of molecules with machine learning, it is desirable to
incorporate $ SO (3) $-covariance. While such models based on low body order features are …
incorporate $ SO (3) $-covariance. While such models based on low body order features are …
A geometry-enhanced graph neural network for learning the smoothness of glassy dynamics from static structure
Modeling the dynamics of glassy systems has been challenging in physics for several
decades. Recent studies have shown the efficacy of Graph Neural Networks (GNNs) in …
decades. Recent studies have shown the efficacy of Graph Neural Networks (GNNs) in …
Complete and Efficient Covariants for Three-Dimensional Point Configurations with Application to Learning Molecular Quantum Properties
When physical properties of molecules are being modeled with machine learning, it is
desirable to incorporate SO (3)-covariance. While such models based on low body order …
desirable to incorporate SO (3)-covariance. While such models based on low body order …
Capturing short-range order in high-entropy alloys with machine learning potentials
Chemical short-range order (SRO) affects the distribution of elements throughout the solid-
solution phase of metallic alloys, thereby modifying the background against which …
solution phase of metallic alloys, thereby modifying the background against which …
Mean-field density matrix decompositions
JJ Eriksen - The Journal of Chemical Physics, 2020 - pubs.aip.org
We introduce new and robust decompositions of mean-field Hartree–Fock and Kohn–Sham
density functional theory relying on the use of localized molecular orbitals and physically …
density functional theory relying on the use of localized molecular orbitals and physically …
Weisfeiler Leman for Euclidean Equivariant Machine Learning
The $ k $-Weifeiler-Leman ($ k $-WL) graph isomorphism test hierarchy is a common
method for assessing the expressive power of graph neural networks (GNNs). Recently, the …
method for assessing the expressive power of graph neural networks (GNNs). Recently, the …
Integer linear programming for unsupervised training set selection in molecular machine learning
Integer linear programming (ILP) is an elegant approach to solve linear optimization
problems, naturally described using integer decision variables. Within the context of physics …
problems, naturally described using integer decision variables. Within the context of physics …