发表机构
Stanford University; SLAC National Accelerator Laboratory（斯坦福大学; SLAC国家加速器实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
libterachem.py是一个Python接口和框架，利用TeraChem的GPU操作实现电子结构方法的模块化开发，支持操作层和方法层的编程，且计算开销低，多GPU扩展性好，并展示了在QM/MM等应用中的高性能。
AI 中文摘要
我们介绍了libterachem.py，这是一个Python接口和方法框架，旨在利用TeraChem优化的图形处理单元（GPU）操作，促进电子结构方法的开发和原型设计。直接访问这些构建模块及其中间量，使用户能够在Python中组装和修改方法，同时重用底层的GPU实现。该框架采用分层架构，可在操作层和方法层进行编程，支持数值实验、使用可互换组件组合完整的电子结构方法，以及与外部科学库的集成。我们通过最小二乘张量超收缩的网格剪枝以及通过LibXC和机器学习Skala泛函提供的交换相关泛函，展示了操作层的使用。我们通过色散校正、周期性计算和外部优化器的几何优化，展示了方法层的使用。使用libterachem.py进行的Python驱动的自洽场计算，与原生TeraChem相比，没有显著的计算开销，并保持了其多GPU扩展性。光活性黄蛋白QM/MM分子动力学应用进一步证明了在与外部模拟库组合的工作流程中，吞吐量与原生TeraChem相当。这些结果确立了libterachem.py作为方法开发和可互操作分子模拟的平台，同时保留了TeraChem GPU操作的性能。
英文摘要
We present libterachem.py, a Python interface and method framework that facilitates development and prototyping of electronic structure methods while leveraging TeraChem's optimized graphical processing unit (GPU) operations. Direct access to these building blocks and their intermediate quantities allows users to assemble and modify methods within Python while reusing the underlying GPU implementations. A layered architecture, programmable at both the operation level and the method level, supports numerical experimentation, composition of complete electronic structure methods with interchangeable components, and integration with external scientific libraries. We illustrate operation-level use with grid pruning for least-squares tensor hypercontraction and with exchange--correlation functionals supplied through LibXC and by the machine-learned Skala functional. We illustrate method-level use with dispersion corrections, periodic calculations, and geometry optimization with an external optimizer. Python-driven self-consistent-field calculations using libterachem.py incur no significant computational overhead compared to native TeraChem and preserve its multi-GPU scaling. A photoactive yellow protein QM/MM molecular dynamics application further demonstrates throughput comparable to native TeraChem in a workflow composed with external simulation libraries. These results establish libterachem.py as a platform for method development and interoperable molecular simulation that retains the performance of TeraChem's GPU operations.
