Gaussian process regression for materials and molecules
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 …
[HTML][HTML] Perspective on integrating machine learning into computational chemistry and materials science
Machine learning (ML) methods are being used in almost every conceivable area of
electronic structure theory and molecular simulation. In particular, ML has become firmly …
electronic structure theory and molecular simulation. In particular, ML has become firmly …
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 …
Machine learning-assisted selection of active spaces for strongly correlated transition metal systems
Active space quantum chemical methods could provide very accurate description of strongly
correlated electronic systems, which is of tremendous value for natural sciences. The proper …
correlated electronic systems, which is of tremendous value for natural sciences. The proper …
Machine-learned energy functionals for multiconfigurational wave functions
We introduce multiconfiguration data-driven functional methods (MC-DDFMs), a group of
methods which aim to correct the total or classical energy of a qualitatively accurate …
methods which aim to correct the total or classical energy of a qualitatively accurate …
Mutual information prediction for strongly correlated systems
We have trained a new machine-learning (ML) model which predicts mutual information (MI)
for strongly correlated systems. This is a complex quantity, which is much more difficult to …
for strongly correlated systems. This is a complex quantity, which is much more difficult to …
Impact of quantum-chemical metrics on the machine learning prediction of electron density
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 …
Learning the exciton properties of azo-dyes
The ab initio determination of electronic excited state (ES) properties is the cornerstone of
theoretical photochemistry. Yet, traditional ES methods become impractical when applied to …
theoretical photochemistry. Yet, traditional ES methods become impractical when applied to …
Local kernel regression and neural network approaches to the conformational landscapes of oligopeptides
The application of machine learning to theoretical chemistry has made it possible to
combine the accuracy of quantum chemical energetics with the thorough sampling of finite …
combine the accuracy of quantum chemical energetics with the thorough sampling of finite …