Enhancing GPU‐Acceleration in the Python‐Based Simulations of Chemistry Frameworks

• DOI: 10.1002/wcms.70008
• Publication Date: 2025-03-24T01:34:22Z
• Authors: Xiaojie Wu; Qiming Sun; Zhichen Pu; Tianze Zheng; Wenzhi Ma; Wen Yan; Yu Xia; Zhengxiao Wu; Mian Huo; Xiang Li; Weiluo Ren; Sheng Gong; Yumin Zhang; Weihao Gao

ABSTRACTWe describe our contribution as industrial stakeholders to the existing open‐source GPU4PySCF project (https://github.com/pyscf/gpu4pyscf), a GPU‐accelerated Python quantum chemistry package. We have integrated GPU acceleration into other PySCF functionalities including Density Functional Theory (DFT), geometry optimization, frequency analysis, solvent models, and the density fitting technique. Through these contributions, GPU4PySCF v1.0 can now be regarded as a fully functional and industrially relevant platform, which we demonstrate in this work through a range of tests. When performing DFT calculations with the density fitting scheme on modern GPU platforms, GPU4PySCF delivers a 30 times speedup over a 32‐core CPU node, resulting in approximately 90% cost savings for most DFT tasks. The performance advantages and productivity improvements have been found in multiple industrial applications, such as generating potential energy surfaces, analyzing molecular properties, calculating solvation free energy, identifying chemical reactions in lithium‐ion batteries, and accelerating neural‐network methods. With the improved design that makes it easy to integrate with the Python and PySCF ecosystem, GPU4PySCF is a natural choice that we can now recommend for many industrial quantum chemistry applications.