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 …
Machine learning for electronically excited states of molecules
J Westermayr, P Marquetand - Chemical Reviews, 2020 - ACS Publications
Electronically excited states of molecules are at the heart of photochemistry, photophysics,
as well as photobiology and also play a role in material science. Their theoretical description …
as well as photobiology and also play a role in material science. Their theoretical description …
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 …
Electronic-structure properties from atom-centered predictions of the electron density
The electron density of a molecule or material has recently received major attention as a
target quantity of machine-learning models. A natural choice to construct a model that yields …
target quantity of machine-learning models. A natural choice to construct a model that yields …
Quantum chemical roots of machine-learning molecular similarity descriptors
In this work, we explore the quantum chemical foundations of descriptors for molecular
similarity. Such descriptors are key for traversing chemical compound space with machine …
similarity. Such descriptors are key for traversing chemical compound space with machine …
Predicting the charge density response in metal electrodes
The computational study of energy storage and conversion processes calls for simulation
techniques that can reproduce the electronic response of metal electrodes under electric …
techniques that can reproduce the electronic response of metal electrodes under electric …
Exchange spin coupling from Gaussian process regression
Heisenberg exchange spin coupling between metal centers is essential for describing and
understanding the electronic structure of many molecular catalysts, metalloenzymes, and …
understanding the electronic structure of many molecular catalysts, metalloenzymes, and …
Learning on-top: Regressing the on-top pair density for real-space visualization of electron correlation
The on-top pair density [Π r] is a local quantum-chemical property that reflects the probability
of two electrons of any spin to occupy the same position in space. Being the simplest …
of two electrons of any spin to occupy the same position in space. Being the simplest …
Impact of quantum-chemical metrics on the machine learning prediction of electron density
KR Briling, A Fabrizio, C Corminboeuf - The Journal of Chemical …, 2021 - pubs.aip.org
Machine learning (ML) algorithms have undergone an explosive development impacting
every aspect of computational chemistry. To obtain reliable predictions, one needs to …
every aspect of computational chemistry. To obtain reliable predictions, one needs to …
[PDF][PDF] Quantum chemical roots of machine-learning molecular similarity
In this work, we explore the quantum chemical foundations of descriptors for molecular
similarity. Such descriptors are key for traversing chemical compound space with machine …
similarity. Such descriptors are key for traversing chemical compound space with machine …