AmberTorchPB: A Unified Framework for Poisson–Boltzmann-Based Reaction Field Energy Calculation via Tensor Computation

Electrostatic interactions are pivotal to understanding biomolecular structure and function, with the Poisson-Boltzmann (PB) equation serving as a cornerstone for modeling these phenomena in ionic solutions; however, the application of PB solvers to large-scale macromolecular assemblies is currently impeded by significant computational bottlenecks and a fragmented software ecosystem rooted in legacy architectures, which collectively struggle to exploit the capabilities of modern high-performance computing (HPC). While traditional methods grapple with scalability and hardware adaptation, tensor abstraction utilized in contemporary deep learning has emerged as a transformative paradigm for efficiently managing hardware heterogeneity, memory optimization, and mixed-precision arithmetic. Capitalizing on this advancement, we introduce AmberTorchPB, a unified, extensible, and accelerator-aware framework built upon LibTorch designed to modernize biomolecular electrostatics. By abstracting low-level data management, AmberTorchPB enables a single algorithmic implementation to seamlessly support diverse sparse matrix layouts, numerical precisions, and computing devices. We demonstrate the framework's versatility by implementing and benchmarking a suite of iterative solvers, thereby providing a robust C++ backend that facilitates rapid prototyping, rigorous benchmarking, and the deployment of high-fidelity electrostatic simulations on heterogeneous architectures.