GPU-Accelerated Large-Scale Nonadiabatic Excited State Molecular Dynamics
7 affiliations
- 1Theoretical Division
- 2Los Alamos National Laboratory
- 3Departamento de Ciencia y Tecnologia
- 4Universidad Nacional de Quilmes/CONICET
- 5Department of Chemistry
- 6University of Florida
- 7Quantum Theory Project
Abstract
Nonadiabatic molecular dynamics is essential for simulating photoinduced processes, but its application to large molecular systems is often limited by the cost of repeatedly evaluating excited-state energies, forces, and nonadiabatic couplings. Here, we present a GPU-accelerated implementation of trajectory surface hopping within PYSEQM (https://github.com/lanl/PYSEQM/), a PyTorch-based semiempirical quantum chemistry framework. The module combines semiempirical model Hamiltonians with configuration-interaction singles excited states and introduces the nonadiabatic coupling vectors and time-derivative couplings required for surface-hopping dynamics. The implementation further includes treatments of trivial crossings and electronic decoherence, providing a complete framework for propagating coupled electronic and nuclear dynamics. To improve GPU utilization, independent trajectories are propagated concurrently in batches. Benchmark calculations show that this strategy substantially increases aggregate simulation throughput as the batch size is increased for small molecules. We demonstrate the applicability of the method to large molecular systems by simulating the excited-state population dynamics of methyl-and tert-butyl substituted molecular nanostar dendrimers, which contain dense manifolds of electronically excited states. These calculations show that semiempirical surface-hopping dynamics can be extended to molecular systems that remain challenging for conventional ab-initio approaches. In addition, the differentiable PyTorch formulation provides a data-science route for optimizing semiempirical Hamiltonian parameters against reference excited-state properties and, ultimately, dynamical observables. PYSEQM therefore provides a platform for high-throughput nonadiabatic molecular dynamics and for the development of data-driven semiempirical models for photophysics and photochemistry.
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BibTeX
@misc{athavale2026gpuaccelerated,
title = {{GPU-Accelerated Large-Scale Nonadiabatic Excited State Molecular Dynamics}},
author = {Vishikh Athavale and Sebastian Fernandez-Alberti and Adrian E. Roitberg and Maksim Kulichenko and Anders M. N. Niklasson and Sergei Tretiak},
howpublished = {ChemRxiv},
year = {2026},
month = sep,
doi = {10.26434/chemrxiv.15008290/v1},
url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008290/v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - GPU-Accelerated Large-Scale Nonadiabatic Excited State Molecular Dynamics
AU - Vishikh Athavale
AU - Sebastian Fernandez-Alberti
AU - Adrian E. Roitberg
AU - Maksim Kulichenko
AU - Anders M. N. Niklasson
AU - Sergei Tretiak
PY - 2026
DA - 2026/09/04
PB - ChemRxiv
DO - 10.26434/chemrxiv.15008290/v1
UR - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008290/v1
AB - Nonadiabatic molecular dynamics is essential for simulating photoinduced processes, but its application to large molecular systems is often limited by the cost of repeatedly evaluating excited-state energies, forces, and nonadiabatic couplings. Here, we present a GPU-accelerated implementation of trajectory surface hopping within PYSEQM (https://github.com/lanl/PYSEQM/), a PyTorch-based semiempirical quantum chemistry framework. The module combines semiempirical model Hamiltonians with configuration-interaction singles excited states and introduces the nonadiabatic coupling vectors and time-derivative couplings required for surface-hopping dynamics. The implementation further includes treatments of trivial crossings and electronic decoherence, providing a complete framework for propagating coupled electronic and nuclear dynamics. To improve GPU utilization, independent trajectories are propagated concurrently in batches. Benchmark calculations show that this strategy substantially increases aggregate simulation throughput as the batch size is increased for small molecules. We demonstrate the applicability of the method to large molecular systems by simulating the excited-state population dynamics of methyl-and tert-butyl substituted molecular nanostar dendrimers, which contain dense manifolds of electronically excited states. These calculations show that semiempirical surface-hopping dynamics can be extended to molecular systems that remain challenging for conventional ab-initio approaches. In addition, the differentiable PyTorch formulation provides a data-science route for optimizing semiempirical Hamiltonian parameters against reference excited-state properties and, ultimately, dynamical observables. PYSEQM therefore provides a platform for high-throughput nonadiabatic molecular dynamics and for the development of data-driven semiempirical models for photophysics and photochemistry.
ER -