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 …

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 …

[HTML][HTML] Perspective on integrating machine learning into computational chemistry and materials science

J Westermayr, M Gastegger, KT Schütt… - The Journal of Chemical …, 2021 - pubs.aip.org
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 …

Machine learning of reactive potentials

Y Yang, S Zhang, KD Ranasinghe… - Annual Review of …, 2024 - annualreviews.org
In the past two decades, machine learning potentials (MLPs) have driven significant
developments in chemical, biological, and material sciences. The construction and training …

Analogies between photochemical reactions and ground-state post-transition-state bifurcations shed light on dynamical origins of selectivity

Z Feng, W Guo, WY Kong, D Chen, S Wang… - Nature Chemistry, 2024 - nature.com
Revealing the origins of kinetic selectivity is one of the premier tasks of applied theoretical
organic chemistry, and for many reactions, doing so involves comparing competing …

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 …

A look inside the black box of machine learning photodynamics simulations

J Li, SA Lopez - Accounts of Chemical Research, 2022 - ACS Publications
Conspectus Photochemical reactions are of great importance in chemistry, biology, and
materials science because they take advantage of a renewable energy source, mild reaction …

WS22 database, Wigner Sampling and geometry interpolation for configurationally diverse molecular datasets

M Pinheiro Jr, S Zhang, PO Dral, M Barbatti - Scientific Data, 2023 - nature.com
Multidimensional surfaces of quantum chemical properties, such as potential energies and
dipole moments, are common targets for machine learning, requiring the development of …

Potential application of machine-learning-based quantum chemical methods in environmental chemistry

D **a, J Chen, Z Fu, T Xu, Z Wang, W Liu… - Environmental …, 2022 - ACS Publications
It is an important topic in environmental sciences to understand the behavior and toxicology
of chemical pollutants. Quantum chemical methodologies have served as useful tools for …

Machine-learning photodynamics simulations uncover the role of substituent effects on the photochemical formation of cubanes

J Li, R Stein, DM Adrion, SA Lopez - Journal of the American …, 2021 - ACS Publications
Photochemical [2+ 2]-cycloadditions store solar energy in chemical bonds and efficiently
access strained organic molecular architectures. Functionalized [3]-ladderdienes undergo …