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 …

High-fidelity first principles nonadiabaticity: Diabatization, analytic representation of global diabatic potential energy matrices, and quantum dynamics

Y Guan, C Xie, DR Yarkony, H Guo - Physical Chemistry Chemical …, 2021 - pubs.rsc.org
Nonadiabatic dynamics, which goes beyond the Born–Oppenheimer approximation, has
increasingly been shown to play an important role in chemical processes, particularly those …

Diabatic states of molecules

Y Shu, Z Varga, S Kanchanakungwankul… - The Journal of …, 2022 - ACS Publications
Quantitative simulations of electronically nonadiabatic molecular processes require both
accurate dynamics algorithms and accurate electronic structure information. Direct …

Advances and new challenges to bimolecular reaction dynamics theory

J Li, B Zhao, D Xie, H Guo - The Journal of Physical Chemistry …, 2020 - ACS Publications
Dynamics of bimolecular reactions in the gas phase are of foundational importance in
combustion, atmospheric chemistry, interstellar chemistry, and plasma chemistry. These …

PESPIP: Software to fit complex molecular and many-body potential energy surfaces with permutationally invariant polynomials

PL Houston, C Qu, Q Yu, R Conte, A Nandi… - The Journal of …, 2023 - pubs.aip.org
We wish to describe a potential energy surface by using a basis of permutationally invariant
polynomials whose coefficients will be determined by numerical regression so as to …

Machine learning and excited-state molecular dynamics

J Westermayr, P Marquetand - Machine Learning: Science and …, 2020 - iopscience.iop.org
Abstract Machine learning is employed at an increasing rate in the research field of quantum
chemistry. While the majority of approaches target the investigation of chemical systems in …

Diabatization by machine intelligence

Y Shu, DG Truhlar - Journal of Chemical Theory and Computation, 2020 - ACS Publications
Understanding nonadiabatic dynamics is important for chemical and physical processes
involving multiple electronic states. Direct nonadiabatic dynamics simulations are often …

Full-dimensional quantum stereodynamics of the non-adiabatic quenching of OH(A2Σ+) by H2

B Zhao, S Han, CL Malbon, U Manthe, DR Yarkony… - Nature …, 2021 - nature.com
Abstract The Born–Oppenheimer approximation, assuming separable nuclear and
electronic motion, is widely adopted for characterizing chemical reactions in a single …

Extending the Representation of Multistate Coupled Potential Energy Surfaces To Include Properties Operators Using Neural Networks: Application to the 1,21A …

Y Guan, H Guo, DR Yarkony - Journal of Chemical Theory and …, 2019 - ACS Publications
Fitting coupled adiabatic potential energy surfaces using coupled diabatic states enables,
for accessible systems, nonadiabatic dynamics to be performed with unprecedented …

Accurate neural network representation of the ab initio determined spin–orbit Interaction in the diabatic representation including the effects of conical intersections

Y Guan, DR Yarkony - The Journal of Physical Chemistry Letters, 2020 - ACS Publications
A method for fitting ab initio determined spin–orbit coupling interactions, in the Breit–Pauli
approximation, based on quasidiabatic representations using neural network fits is reported …