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Gpu-accelerated Graph-based Semiempirical Quantum Chemistry.

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Gpu-accelerated Graph-based Semiempirical Quantum Chemistry.

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GPU-Accelerated Graph-Based Semiempirical Quantum Chemistry.

Maksim Kulichenko¹, Robert M Stanton¹, Cheng-Han Li¹

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

Journal of Chemical Theory and Computation

|October 27, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

This study couples graph-based electronic structure theory (SEDACS) with GPU-accelerated semiempirical methods (PySEQM) for efficient, large-scale atomistic simulations. The approach achieves significant speedups, reducing computational costs for complex chemistry problems.

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Area of Science:

Computational chemistry
Materials science
Quantum mechanics

Background:

Graph-based electronic structure theory offers scalability for large atomistic systems.
Distributed and hybrid computing platforms are crucial for complex simulations.

Purpose of the Study:

To couple graph-based linear scaling electronic structure theory (SEDACS) with semiempirical quantum chemistry methods (PySEQM).
To leverage Graphics Processing Unit (GPU) acceleration for enhanced computational efficiency.
To enable scalable electronic structure calculations on distributed and hybrid platforms.

Main Methods:

Integration of the Scalable Ecosystem, Driver, and Analyzer for Complex Chemistry Simulations (SEDACS) with the PySEQM code.
Implementation of Graphics Processing Unit (GPU) acceleration within the coupled framework.

Analysis of parallelization efficiency, computational accuracy, and communication overheads.

Main Results:

Achieved efficient and scalable electronic structure calculations across multiple nodes.
Demonstrated an order-of-magnitude speedup for systems up to 10,000 atoms.
Validated the reduction in computational cost and effective harnessing of parallelism.

Conclusions:

The combined SEDACS and PySEQM approach with GPU acceleration provides a powerful tool for large-scale electronic structure studies.
This method significantly reduces computational expense for complex atomistic systems.
The findings pave the way for more accessible and efficient computational chemistry research.