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 …
High-fidelity first principles nonadiabaticity: Diabatization, analytic representation of global diabatic potential energy matrices, and quantum dynamics
Nonadiabatic dynamics, which goes beyond the Born–Oppenheimer approximation, has
increasingly been shown to play an important role in chemical processes, particularly those …
increasingly been shown to play an important role in chemical processes, particularly those …
Diabatic states of molecules
Quantitative simulations of electronically nonadiabatic molecular processes require both
accurate dynamics algorithms and accurate electronic structure information. Direct …
accurate dynamics algorithms and accurate electronic structure information. Direct …
Advances and new challenges to bimolecular reaction dynamics theory
Dynamics of bimolecular reactions in the gas phase are of foundational importance in
combustion, atmospheric chemistry, interstellar chemistry, and plasma chemistry. These …
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
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 …
polynomials whose coefficients will be determined by numerical regression so as to …
Machine learning and excited-state molecular dynamics
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 …
chemistry. While the majority of approaches target the investigation of chemical systems in …
Fitting of Coupled Potential Energy Surfaces via Discovery of Companion Matrices by Machine Intelligence
Fitting coupled potential energy surfaces is a critical step in simulating electronically
nonadiabatic chemical reactions and energy transfer processes. Analytic representation of …
nonadiabatic chemical reactions and energy transfer processes. Analytic representation of …
Diabatization by machine intelligence
Understanding nonadiabatic dynamics is important for chemical and physical processes
involving multiple electronic states. Direct nonadiabatic dynamics simulations are often …
involving multiple electronic states. Direct nonadiabatic dynamics simulations are often …
Full-dimensional quantum stereodynamics of the non-adiabatic quenching of OH(A2Σ+) by H2
Abstract The Born–Oppenheimer approximation, assuming separable nuclear and
electronic motion, is widely adopted for characterizing chemical reactions in a single …
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 …
Fitting coupled adiabatic potential energy surfaces using coupled diabatic states enables,
for accessible systems, nonadiabatic dynamics to be performed with unprecedented …
for accessible systems, nonadiabatic dynamics to be performed with unprecedented …