2026-10-11T22:55:25Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33806869 2026-09-15T16:39:40Z portal_1197 item_type_3 month_year_09_2026

Data for Direct grid-based nonadiabatic dynamics on machine-learned potential energy surfaces : application to spin-forbidden processes Gareth W. Richings (5263193) Scott Habershon (1263891) Quantum chemistry Quantum chemistry -- Computer programs Gaussian processes Machine learning Kernel functions Quantum theory Migrated from ePrints We have recently shown how high-accuracy wavefunction grid-based propagation schemes, such as the multiconfiguration time-dependent Hartree (MCTDH) method, can be combined with machine-learning (ML) descriptions of PESs to yield an `on-the-fly' direct dynamics scheme which circumvents PES pre-fitting. To date, our approach has been demonstrated in the ground-state dynamics and non-adiabatic spin-allowed dynamics of several molecular systems. Expanding on this successful previous work, this Article demonstrates how our ML-based quantum dynamics scheme can be adapted to model non-adiabatic dynamics for spin-forbidden processes such as inter-system crossing (ISC), opening up new possibilities for modelling chemical dynamic phenomena driven by spin-orbit coupling. After describing modifications to diabatization schemes to enable accurate and robust treatment or electronic states of different spin-multiplicity, we demonstrate our methodology in applications to modelling ISC in SO2 and thioformaldehyde, benchmarking our results against previous trajectory-based calculations. As a relatively efficient tool for modelling spin-forbidden non-adiabatic dynamics without demanding any pre-fitting of PESs, our overall strategy is a potentially powerful tool for modelling important photochemical systems, such as photoactivated pro-drugs and organometallic catalysts.<br><br>Data record consists of a single zip archive, organised into directories, also containing an Icon file and an accompanying Readme file. This directories contain the data for Figures 1-6 in the corresponding publication. The data are given as either ASCII text files, or in the form of GNUplot files. Detail on the directories for each file and its corresponding figure can be found in the accompanying readme file. 2026-09-15T16:39:40Z Dataset Dataset 10.82444/warw.33806869.v1 https://figshare.com/articles/dataset/Data_for_Direct_grid-based_nonadiabatic_dynamics_on_machine-learned_potential_energy_surfaces_application_to_spin-forbidden_processes/33806869 CC BY 4.0