Polarons in materials

C Franchini, M Reticcioli, M Setvin… - Nature Reviews Materials, 2021 - nature.com
Polarons are quasiparticles that easily form in polarizable materials due to the coupling of
excess electrons or holes with ionic vibrations. These quasiparticles manifest themselves in …

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 …

Complex reaction processes in combustion unraveled by neural network-based molecular dynamics simulation

J Zeng, L Cao, M Xu, T Zhu, JZH Zhang - Nature communications, 2020 - nature.com
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 …

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 …

Ab initio machine learning in chemical compound space

B Huang, OA Von Lilienfeld - Chemical reviews, 2021 - ACS Publications
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 …

Quantum machine learning for chemistry and physics

M Sajjan, J Li, R Selvarajan, SH Sureshbabu… - Chemical Society …, 2022 - pubs.rsc.org
Machine learning (ML) has emerged as a formidable force for identifying hidden but
pertinent patterns within a given data set with the objective of subsequent generation of …

Combining SchNet and SHARC: The SchNarc machine learning approach for excited-state dynamics

J Westermayr, M Gastegger… - The journal of physical …, 2020 - ACS Publications
In recent years, deep learning has become a part of our everyday life and is revolutionizing
quantum chemistry as well. In this work, we show how deep learning can be used to …

The rise of neural networks for materials and chemical dynamics

M Kulichenko, JS Smith, B Nebgen, YW Li… - The Journal of …, 2021 - ACS Publications
Machine learning (ML) is quickly becoming a premier tool for modeling chemical processes
and materials. ML-based force fields, trained on large data sets of high-quality electron …

Deep learning study of tyrosine reveals that roaming can lead to photodamage

J Westermayr, M Gastegger, D Vörös… - Nature Chemistry, 2022 - nature.com
Amino acids are among the building blocks of life, forming peptides and proteins, and have
been carefully 'selected'to prevent harmful reactions caused by light. To prevent …

Reaction-based machine learning representations for predicting the enantioselectivity of organocatalysts

S Gallarati, R Fabregat, R Laplaza, S Bhattacharjee… - Chemical …, 2021 - pubs.rsc.org
Hundreds of catalytic methods are developed each year to meet the demand for high-purity
chiral compounds. The computational design of enantioselective organocatalysts remains a …