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High-Performance Semiempirical Excited-State Molecular Dynamics Powered by Graphics Processing Units
Vishikh Athavale1, Maksim Kulichenko1, Sebastian Fernandez-Alberti2
1Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States.
The Journal of Physical Chemistry Letters
|February 19, 2026
Summary
This study introduces PYSEQM, a GPU-accelerated engine for excited-state molecular dynamics (ESMD) simulations. It enables efficient, long-timescale simulations of molecular systems and spectra computation using machine learning integration.
Area of Science:
- Quantum Chemistry
- Computational Chemistry
- Spectroscopy
Background:
- Excited-state molecular dynamics (ESMD) is crucial for understanding photophysical processes.
- Simulating long trajectories and large ensembles for excited states is computationally demanding.
- Existing methods often struggle with efficiency and scalability for complex systems.
Purpose of the Study:
- Introduce PYSEQM, a GPU-accelerated engine for efficient excited-state molecular dynamics.
- Implement and validate an extended Lagrangian excited-state Born-Oppenheimer molecular dynamics (XL-ESMD) scheme.
- Demonstrate the platform's capability for computing spectroscopic properties and its potential for machine learning integration.
Main Methods:
- Developed an excited-state Born-Oppenheimer molecular dynamics (BOMD) module within the PYSEQM engine using PyTorch.
- Implemented an extended Lagrangian excited-state BOMD (XL-ESMD) scheme for improved efficiency and convergence.
- Utilized GPU acceleration and batched execution for high-throughput simulations.
- Propagated trajectories on ground and excited states to compute absorption, emission, and infrared spectra.
Main Results:
- Achieved efficient simulation of long trajectories and large statistical ensembles on a single GPU.
- Demonstrated smooth scaling from small molecules to a 900-atom dendrimer.
- XL-ESMD scheme provided accurate spectra at significantly reduced computational cost.
- PYSEQM's PyTorch foundation enables automatic differentiation, GPU batching, and ML model integration.
Conclusions:
- PYSEQM provides a practical and efficient platform for excited-state molecular dynamics simulations.
- The XL-ESMD scheme is effective for cost-efficient excited-state BOMD.
- The platform facilitates machine learning-augmented dynamics and future data-driven nonadiabatic modeling.
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