  We 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 functionality including Density Functional Theory
(DFT), geometry optimization, frequency analysis, solvent models, and 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 on modern GPU platforms, GPU4PySCF delivers 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 natural choice that
we can now recommend for many industrial quantum chemistry applications.
