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-learned potentials for next-generation matter simulations
The choice of simulation methods in computational materials science is driven by a
fundamental trade-off: bridging large time-and length-scales with highly accurate …
fundamental trade-off: bridging large time-and length-scales with highly accurate …
Machine learning force fields
In recent years, the use of machine learning (ML) in computational chemistry has enabled
numerous advances previously out of reach due to the computational complexity of …
numerous advances previously out of reach due to the computational complexity of …
The OpenMolcas Web: A Community-Driven Approach to Advancing Computational Chemistry
The developments of the open-source OpenMolcas chemistry software environment since
spring 2020 are described, with a focus on novel functionalities accessible in the stable …
spring 2020 are described, with a focus on novel functionalities accessible in the stable …
A Euclidean transformer for fast and stable machine learned force fields
Recent years have seen vast progress in the development of machine learned force fields
(MLFFs) based on ab-initio reference calculations. Despite achieving low test errors, the …
(MLFFs) based on ab-initio reference calculations. Despite achieving low test errors, the …
Machine learning for electronically excited states of molecules
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 …
Quantum chemistry in the age of machine learning
PO Dral - The journal of physical chemistry letters, 2020 - ACS Publications
As the quantum chemistry (QC) community embraces machine learning (ML), the number of
new methods and applications based on the combination of QC and ML is surging. In this …
new methods and applications based on the combination of QC and ML is surging. In this …
Complex reaction processes in combustion unraveled by neural network-based molecular dynamics simulation
Combustion is a complex chemical system which involves thousands of chemical reactions
and generates hundreds of molecular species and radicals during the process. In this work …
and generates hundreds of molecular species and radicals during the process. In this work …
Choosing the right molecular machine learning potential
Quantum-chemistry simulations based on potential energy surfaces of molecules provide
invaluable insight into the physicochemical processes at the atomistic level and yield such …
invaluable insight into the physicochemical processes at the atomistic level and yield such …
Molecular excited states through a machine learning lens
Theoretical simulations of electronic excitations and associated processes in molecules are
indispensable for fundamental research and technological innovations. However, such …
indispensable for fundamental research and technological innovations. However, such …