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  3. High-performance Semiempirical Excited-state Molecular Dynamics Powered By Graphics Processing Units.
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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

View abstract on PubMed

Summary
This summary is machine-generated.

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.

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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.