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Accelerating Density Fitting with Adaptive Precision and 8-Bit Integer on AI Accelerators
Hua Huang1, Wenkai Shao2, Jeff Hammond3
1NVIDIA, Santa Clara, California 94086, United States.
The Journal of Physical Chemistry. A
|April 17, 2026
Summary
We developed an adaptive precision algorithm for AI accelerators to speed up quantum chemistry calculations. This method accelerates density fitting using 8-bit integer (INT8) arithmetics without sacrificing accuracy.
Area of Science:
- Computational Chemistry
- Artificial Intelligence Hardware
Background:
- Artificial intelligence (AI) accelerators offer potential for scientific computation speedups.
- Quantum chemistry simulations face challenges with AI hardware due to accuracy demands and data patterns.
Purpose of the Study:
- To develop an adaptive precision algorithm for accelerating density fitting (DF) in quantum chemistry on AI accelerators.
- To utilize 8-bit integer (INT8) arithmetics for enhanced computational efficiency.
Main Methods:
- Implementation of an adaptive precision algorithm within the GPU-accelerated PySCF package.
- Testing the algorithm on over 20 molecular systems across various NVIDIA GPUs.
- Utilizing 8-bit integer (INT8) arithmetic for tensor computations.
Main Results:
- Achieved speedups of up to 204% on an RTX 4090 and 364% on an RTX 6000 Ada workstation GPU compared to standard FP64 calculations.
- Maintained converged energy accuracy, demonstrating no compromise in simulation reliability.
- Demonstrated practical application of AI hardware for quantum chemistry.
Conclusions:
- The proposed adaptive precision algorithm effectively accelerates density fitting in quantum chemistry on AI accelerators.
- INT8 arithmetics can be reliably used for high-accuracy quantum chemistry simulations.
- AI hardware presents a viable pathway for efficient and accurate quantum chemistry simulations.
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