Paper: Quantum Chemistry Acceleration - Comparative Performance Analysis of Modern DFT Implementations #1093
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Hello, I was assigned to review this. I will do a pass by the weekend, overall on a quick glance this looks good. |
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Inviting reviewers: @nyetsche and @mihaimaruseac |
papers/kyohei_sahara/main.md
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| # Ensure that this title is the same as the one in `myst.yml` | ||
| title: "Quantum Chemistry Acceleration: Comparative Performance Analysis of Modern DFT Implementations" | ||
| abstract: | | ||
| This proceeding examines the acceleration of quantum chemistry calculations through |
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| This proceeding examines the acceleration of quantum chemistry calculations through | |
| This article examines the acceleration of quantum chemistry calculations through |
papers/kyohei_sahara/main.md
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| - **Enhanced statistical reliability**: Larger sample sizes enabling more robust conclusions and confident predictions in materials design studies through increased sampling of molecular configurations and property distributions | ||
| - **Integration with modern workflows**: Seamless compatibility with machine learning approaches and automated high-throughput computational screening pipelines, facilitating development of predictive models for materials discovery | ||
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| The methodology established here provides a foundation for accelerating quantum chemistry calculations across various molecular systems, particularly those involving compounds with heteroatoms and solvation effects. |
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This is not my scientific domain so perhaps heteroatoms and solvation effects can be defined in parentheses? If the definition is obvious a domain expert, then no need.
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This is addressed in the latest edits: "The methodology established here provides a foundation for accelerating quantum chemistry calculations across various molecular systems, particularly those involving compounds with heteroatoms (atoms other than carbon and hydrogen) and solvation effects (interactions between solutes and surrounding solvent molecules)."
papers/kyohei_sahara/main.md
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| SIESTA have been widely adopted due to their robust implementations and | ||
| extensive feature sets. However, these frameworks often present computational | ||
| bottlenecks when applied to complex systems requiring extensive parameter space | ||
| exploration. |
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these frameworks - is it just SIESTA, or are there others?
As a general comment to the paper, I come away with the understanding that PySCF and GPU4PySCF are much faster and cheaper than SIESTA, but I'm not entirely sure why they're faster. Is it improved parallelization (MPI and particularly on GPUs)? Algorithmic breakthroughs? Is there any case where SIESTA would still be a better choice due to the problem/scientific aspects?
There's probably not enough space to go into details on the PySCF/GPU4PySCF architecture or algorithms, but maybe a sentence or two would help, example: PySCF uses the parallel Foo-Bar Algorithm [2017, Foo et al) to calculate the Frobius coefficient effectively.
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This has been addressed in the latest edits. "PySCF achieves performance improvements through efficient integral computation algorithms and optimized parallel processing using OpenMP and MPI implementations, while GPU4PySCF further accelerates calculations by leveraging GPU parallelization for computationally intensive operations like two-electron integrals and matrix operations."
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Not my field, so I can't speak of the scientific merits, but overall a solid paper. |
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@cdlindsey will serve as editor for this paper. |
Summary
This paper presents a performance analysis of modern DFT implementations for quantum chemistry calculations, comparing SIESTA, PySCF, and GPU4PySCF on 38-atom chlorinated phosphoric acid systems.
Key Results
Code Availability
https://github.com/schwalbe10/quantum-chemistry-acceleration
This is a draft submission for review.