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PYSEQM 2.0: Accelerated Semiempirical Excited-State Calculations on Graphical Processing Units.

Vishikh Athavale¹, Nikita Fedik¹, William Colglazier^1,2

¹Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States.

Journal of Chemical Theory and Computation

|September 25, 2025

Summary

Researchers developed new computational methods for calculating electronic excited states in large molecules using PYSEQM 2.0. This enables faster simulations for photochemistry and materials science applications.

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Area of Science:

Computational Chemistry
Quantum Mechanics
Photochemistry

Background:

Semiempirical quantum chemical methods are essential for studying molecular properties.
Calculating electronic excited states is computationally demanding, especially for large systems.
Existing methods lack efficiency for large-scale simulations.

Purpose of the Study:

To develop and implement electronic excited-state capabilities for semiempirical quantum chemical methods.
To enhance the PYSEQM 2.0 software package for efficient simulations.
To enable accurate excited-state property calculations for large molecular systems.

Main Methods:

Configuration Interaction Singles (CIS) and Time-Dependent Hartree-Fock (TDHF) levels of theory were implemented.
The PYSEQM 2.0 software package, utilizing PyTorch for GPU acceleration and automatic differentiation, was employed.
Benchmarking was performed on systems up to a thousand atoms.

Main Results:

Excited-state computations for large systems were achieved in under a minute on modern GPUs.
PYSEQM 2.0 demonstrated substantial performance gains in molecular property evaluations.
A machine learning interface for Hamiltonian parameter reoptimization and neural network training was integrated.

Conclusions:

The developed methods facilitate access to excited-state quantum chemistry for large systems.
The approach is suitable for high-throughput screening, real-time feedback, and large-scale dynamical studies.
This work lays the foundation for hybrid quantum-machine-learning approaches in photochemistry, photophysics, and materials discovery.