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 …
Molecular excited states through a machine learning lens
PO Dral, M Barbatti - Nature Reviews Chemistry, 2021 - nature.com
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 …
[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 …
Diabatic states of molecules
Quantitative simulations of electronically nonadiabatic molecular processes require both
accurate dynamics algorithms and accurate electronic structure information. Direct …
accurate dynamics algorithms and accurate electronic structure information. Direct …
Excited state non-adiabatic dynamics of large photoswitchable molecules using a chemically transferable machine learning potential
S Axelrod, E Shakhnovich… - Nature …, 2022 - nature.com
Light-induced chemical processes are ubiquitous in nature and have widespread
technological applications. For example, photoisomerization can allow a drug with a photo …
technological applications. For example, photoisomerization can allow a drug with a photo …
Physically inspired deep learning of molecular excitations and photoemission spectra
J Westermayr, RJ Maurer - Chemical Science, 2021 - pubs.rsc.org
Modern functional materials consist of large molecular building blocks with significant
chemical complexity which limits spectroscopic property prediction with accurate first …
chemical complexity which limits spectroscopic property prediction with accurate first …
Semiclassical Multistate Dynamics for Six Coupled 5A′ States of O + O2
Dynamics simulations of high-energy O2–O collisions play an important role in simulating
thermal energy content and heat flux in flows around hypersonic vehicles. To carry out such …
thermal energy content and heat flux in flows around hypersonic vehicles. To carry out such …
Quantum dynamics of photodissociation: recent advances and challenges
Recent advances in constructing accurate potential energy surfaces and nonadiabatic
couplings from high-level ab initio data have revealed detailed potential landscapes in not …
couplings from high-level ab initio data have revealed detailed potential landscapes in not …
Constructing diabatic potential energy matrices with neural networks based on adiabatic energies and physical considerations: Toward quantum dynamic accuracy
A permutation invariant polynomial-neural network (PIP-NN) approach for constructing the
global diabatic potential energy matrices (PEMs) of the coupled states of molecules is …
global diabatic potential energy matrices (PEMs) of the coupled states of molecules is …
Simple and effective screening parameter for range-separated dielectric-dependent hybrids
A simple effective screening parameter for the screened range-separated exchange-
correlation hybrid functional is constructed from the compressibility sum rule, in the context …
correlation hybrid functional is constructed from the compressibility sum rule, in the context …