Polarons in materials
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 …
excess electrons or holes with ionic vibrations. These quasiparticles manifest themselves in …
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 …
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 …
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 …
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 …
Quantum machine learning for chemistry and physics
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 …
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
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 …
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
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 …
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
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 …
been carefully 'selected'to prevent harmful reactions caused by light. To prevent …
Reaction-based machine learning representations for predicting the enantioselectivity of organocatalysts
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 …
chiral compounds. The computational design of enantioselective organocatalysts remains a …