Computational and data driven molecular material design assisted by low scaling quantum mechanics calculations and machine learning

W Li, H Ma, S Li, J Ma - Chemical Science, 2021 - pubs.rsc.org
Electronic structure methods based on quantum mechanics (QM) are widely employed in the
computational predictions of the molecular properties and optoelectronic properties of …

Divide-and-conquer linear-scaling quantum chemical computations

H Nakai, M Kobayashi, T Yoshikawa… - The Journal of …, 2023 - ACS Publications
Fragmentation and embedding schemes are of great importance when applying quantum-
chemical calculations to more complex and attractive targets. The divide-and-conquer (DC) …

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 …

Conical intersection in chemiluminescence of cyclic peroxides

L Yue, YJ Liu - The Journal of Physical Chemistry Letters, 2022 - ACS Publications
Chemiluminescence (CL) utilizing chemiexcitation for energy transformation is one of the
most highly sensitive and useful analytical techniques. The chemiexcitation is a chemical …

Nonadiabatic Exciton Dynamics and Energy Gradients in the Framework of FMO-LC-TDDFTB

R Einsele, R Mitrić - Journal of Chemical Theory and Computation, 2024 - ACS Publications
We introduce a novel methodology for simulating the excited-state dynamics of extensive
molecular aggregates in the framework of the long-range corrected time-dependent density …

Perspective on simplified quantum chemistry methods for excited states and response properties

M de Wergifosse, S Grimme - The Journal of Physical Chemistry A, 2021 - ACS Publications
We review recent developments in the framework of simplified quantum chemistry for excited
state and optical response properties (sTD-DFT) and present future challenges for new …

Machine learning accelerated photodynamics simulations

J Li, SA Lopez - Chemical Physics Reviews, 2023 - pubs.aip.org
Machine learning (ML) continues to revolutionize computational chemistry for accelerating
predictions and simulations by training on experimental or accurate but expensive quantum …

Trajectory surface hop** approach to condensed-phase nonradiative relaxation dynamics using divide-and-conquer spin-flip time-dependent density-functional …

H Uratani, T Yoshikawa, H Nakai - Journal of chemical theory and …, 2021 - ACS Publications
Nonradiative relaxation of excited molecules is central to many crucial issues in
photochemistry. Condensed phases are typical contexts in which such problems are …

Fast nonadiabatic molecular dynamics via spin-flip time-dependent density-functional tight-binding approach: Application to nonradiative relaxation of …

H Uratani, T Morioka, T Yoshikawa… - Journal of chemical …, 2020 - ACS Publications
Nonadiabatic dynamics around conical intersections between ground and excited states are
crucial to understand excited-state phenomena in complex chemical systems. With this …

Understanding intermolecular interactions of large systems in ground state and excited state by using density functional based tight binding methods

Y Xu, R Friedman, W Wu, P Su - The Journal of Chemical Physics, 2021 - pubs.aip.org
A novel energy decomposition analysis scheme, named DFTB-EDA, is proposed based on
the density functional based tight-binding method (DFTB/TD-DFTB), which is a semi …