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 …
Machine learning for chemical reactions
M Meuwly - Chemical Reviews, 2021 - ACS Publications
Machine learning (ML) techniques applied to chemical reactions have a long history. The
present contribution discusses applications ranging from small molecule reaction dynamics …
present contribution discusses applications ranging from small molecule reaction dynamics …
[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 …
Ab initio machine learning in chemical compound space
Chemical compound space (CCS), the set of all theoretically conceivable combinations of
chemical elements and (meta-) stable geometries that make up matter, is colossal. The first …
chemical elements and (meta-) stable geometries that make up matter, is colossal. The first …
Crash testing machine learning force fields for molecules, materials, and interfaces: molecular dynamics in the TEA challenge 2023
We present the second part of the rigorous evaluation of modern machine learning force
fields (MLFFs) within the TEA Challenge 2023. This study provides an in-depth analysis of …
fields (MLFFs) within the TEA Challenge 2023. This study provides an in-depth analysis of …
Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning
Computational study of molecules and materials from first principles is a cornerstone of
physics, chemistry, and materials science, but limited by the cost of accurate and precise …
physics, chemistry, and materials science, but limited by the cost of accurate and precise …
Transfer learning for chemically accurate interatomic neural network potentials
Develo** machine learning-based interatomic potentials from ab initio electronic structure
methods remains a challenging task for computational chemistry and materials science. This …
methods remains a challenging task for computational chemistry and materials science. This …
Transfer learning for affordable and high-quality tunneling splittings from instanton calculations
The combination of transfer learning (TL) a low-level potential energy surface (PES) to a
higher level of electronic structure theory together with ring-polymer instanton (RPI) theory is …
higher level of electronic structure theory together with ring-polymer instanton (RPI) theory is …
Uncertainty quantification for predictions of atomistic neural networks
The value of uncertainty quantification on predictions for trained neural networks (NNs) on
quantum chemical reference data is quantitatively explored. For this, the architecture of the …
quantum chemical reference data is quantitatively explored. For this, the architecture of the …
Permutationally invariant, reproducing kernel-based potential energy surfaces for polyatomic molecules: From formaldehyde to acetone
Constructing accurate, high-dimensional molecular potential energy surfaces (PESs) for
polyatomic molecules is challenging. Reproducing kernel Hilbert space (RKHS) …
polyatomic molecules is challenging. Reproducing kernel Hilbert space (RKHS) …