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 …
Machine learning of reactive potentials
In the past two decades, machine learning potentials (MLPs) have driven significant
developments in chemical, biological, and material sciences. The construction and training …
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
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 …
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 …
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 …
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 …
dipole moments, are common targets for machine learning, requiring the development of …
Potential application of machine-learning-based quantum chemical methods in environmental chemistry
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 …
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 …
access strained organic molecular architectures. Functionalized [3]-ladderdienes undergo …