Gaussian process regression for materials and molecules
VL Deringer, AP Bartók, N Bernstein… - Chemical …, 2021 - ACS Publications
We provide an introduction to Gaussian process regression (GPR) machine-learning
methods in computational materials science and chemistry. The focus of the present review …
methods in computational materials science and chemistry. The focus of the present review …
Physics-inspired structural representations for molecules and materials
The first step in the construction of a regression model or a data-driven analysis, aiming to
predict or elucidate the relationship between the atomic-scale structure of matter and its …
predict or elucidate the relationship between the atomic-scale structure of matter and its …
Equivariant representations for molecular Hamiltonians and N-center atomic-scale properties
Symmetry considerations are at the core of the major frameworks used to provide an
effective mathematical representation of atomic configurations that is then used in machine …
effective mathematical representation of atomic configurations that is then used in machine …
Atomic cluster expansion of scalar, vectorial, and tensorial properties including magnetism and charge transfer
R Drautz - Physical Review B, 2020 - APS
The atomic cluster expansion [R. Drautz, Phys. Rev. B 99, 014104 (2019) 2469-9950
10.1103/PhysRevB. 99.014104] is extended in two ways, the modeling of vectorial and …
10.1103/PhysRevB. 99.014104] is extended in two ways, the modeling of vectorial and …
Multi-scale approach for the prediction of atomic scale properties
Electronic nearsightedness is one of the fundamental principles that governs the behavior of
condensed matter and supports its description in terms of local entities such as chemical …
condensed matter and supports its description in terms of local entities such as chemical …
Prediction of energetic material properties from electronic structure using 3D convolutional neural networks
AD Casey, SF Son, I Bilionis… - Journal of Chemical …, 2020 - ACS Publications
We develop a convolutional neural network capable of directly parsing the 3D electronic
structure of a molecule described by spatial point data for charge density and electrostatic …
structure of a molecule described by spatial point data for charge density and electrostatic …
Learning electron densities in the condensed phase
We introduce a local machine-learning method for predicting the electron densities of
periodic systems. The framework is based on a numerical, atom-centered auxiliary basis …
periodic systems. The framework is based on a numerical, atom-centered auxiliary basis …
Accurate molecular-orbital-based machine learning energies via unsupervised clustering of chemical space
We introduce an unsupervised clustering algorithm to improve training efficiency and
accuracy in predicting energies using molecular-orbital-based machine learning (MOB-ML) …
accuracy in predicting energies using molecular-orbital-based machine learning (MOB-ML) …
Efficient quantum vibrational spectroscopy of water with high-order path integrals: From bulk to interfaces
Vibrational spectroscopy is key in probing the interplay between the structure and dynamics
of aqueous systems. To map different regions of experimental spectra to the microscopic …
of aqueous systems. To map different regions of experimental spectra to the microscopic …
Learning Electronic Polarizations in Aqueous Systems
A Jana, S Shepherd, Y Litman… - Journal of Chemical …, 2024 - ACS Publications
The polarization of periodically repeating systems is a discontinuous function of the atomic
positions, a fact which seems at first to stymie attempts at their statistical learning. Two …
positions, a fact which seems at first to stymie attempts at their statistical learning. Two …