This computational study characterises charge-transfer-to-solvent (CTTS) states of aqueous thiocyanate anion using equation-of-motion coupled-cluster methods combined with electrostatic embedding quantum mechanics/molecular mechanics (QM/MM) scheme. Equilibrium sampling was carried out using classical molecular dynamics (MD) with standard force-fields and QM/MM ab initio molecular dynamics (AIMD) using density functional theory. The two calculations yield significantly different local structure around solvated SCN−
. Because of the diffuse character of CTTS states, they are very sensitive to the local structure of solvent around the solute and its dynamic fluctuations. Owing to this sensitivity, the spectra computed using MD and AIMD based snapshots differ considerably. This sensitivity suggests that the spectroscopy exploiting CTTS transitions can provide an experimental handle for assessing the quality of force-fields and density functionals. By combining CTTS-based spectroscopies with reliable theoretical modeling, detailed microscopic information of the solvent structure can be obtained. We present a robust computational protocol for modeling spectra of solvated anions and emphasise the use of an ab initio characterization of individual electronic transitions as CTTS or local excitations.
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This content will become publicly available on March 7, 2025
Nonadiabatic Dynamics in Two-Dimensional Perovskites Assisted by Machine Learned Force Fields
An exploration of the “on-the-fly” nonadiabatic couplings (NACs) for nonradiative relaxation and recombination of excited states in 2D Dion–Jacobson (DJ) lead halide perovskites (LHPs) is accelerated by a machine learning approach. Specifically, ab initio molecular dynamics (AIMD) of nanostructures composed of heavy elements is performed with the use of machine-learning force-fields (MLFFs), as implemented in the Vienna Ab initio Simulation Package (VASP). The force field parametrization is established using on-the-fly learning, which continuously builds a force field using AIMD data. At each time step of the molecular dynamics (MD) simulation, the total energy and forces are predicted based on the MLFF and if the Bayesian error estimate exceeds a threshold, an ab initio calculation is performed, which is used to construct a new force field. Model training of MLFF and evaluation were performed for a range of DJ-LHP models of different thicknesses and halide compositions. The MLFF-MD trajectories were evaluated against pure AIMD trajectories to assess the level of discrepancy and error accumulation. To examine the practical effectiveness of this approach, we have used the MLFF-based MD trajectories to compute NAC and excited-state dynamics. At each stage, results based on machine learning are compared to traditional ab initio based electronic dissipative dynamics. We find that MLFF-MD provides comparable results to AIMDs when MLFF is trained in an NPT ensemble.
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- Award ID(s):
- 1944921
- PAR ID:
- 10528719
- Publisher / Repository:
- American Chemical Society
- Date Published:
- Journal Name:
- The Journal of Physical Chemistry C
- Volume:
- 128
- Issue:
- 9
- ISSN:
- 1932-7447
- Page Range / eLocation ID:
- 3935 to 3944
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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