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Design and Optimization Strategies of a High-Performance Vented Box
Published on: June 9, 2023
TorchFF: A high-performance GPU-accelerated differentiable force field library
Yingze Wang1, Aalim S Abdullah1, Rohith Srinivaas Mohanakrishnan2
1Kenneth S. Pitzer Theory Center and Department of Chemistry, Berkeley, California 94720, USA.
The Journal of Chemical Physics
|July 29, 2026
Summary
TorchFF is a new library that speeds up molecular dynamics simulations. It uses custom CUDA operators to overcome performance bottlenecks in PyTorch for molecular modeling tasks.
Area of Science:
- Computational chemistry
- Molecular modeling
- Scientific computing
Background:
- Molecular dynamics (MD) simulations and force field development increasingly use automatic differentiation.
- Standard frameworks face performance bottlenecks in MD simulations due to small-kernel operations and memory limitations.
Purpose of the Study:
- To introduce TorchFF, a high-performance, differentiable library for molecular modeling.
- To address performance bottlenecks in PyTorch for MD simulations.
Main Methods:
- Developed TorchFF, extending PyTorch with customized CUDA operators.
- Implemented performance-critical routines (bonded interactions, electrostatics, Ewald summation, neighbor lists) as backend-optimized primitives.
Main Results:
- TorchFF bridges the gap between Python's flexibility and compiled MD engine speeds.
- Achieved high performance for molecular modeling tasks within the PyTorch ecosystem.
Conclusions:
- TorchFF offers a solution for efficient and differentiable molecular dynamics simulations.
- Enables faster property prediction and parameter optimization in molecular modeling.
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