Litman Yair, Kapil Venkat, Feldman Yotam M Y, Tisi Davide, Begušić Tomislav, Fidanyan Karen, Fraux Guillaume, Higer Jacob, Kellner Matthias, Li Tao E, Pós Eszter S, Stocco Elia, Trenins George, Hirshberg Barak, Rossi Mariana, Ceriotti Michele
Y. Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom.
Department of Physics and Astronomy, University College London, 17-19 Gordon St, London WC1H 0AH, United Kingdom.
J Chem Phys. 2024 Aug 14;161(6). doi: 10.1063/5.0215869.
Atomic-scale simulations have progressed tremendously over the past decade, largely thanks to the availability of machine-learning interatomic potentials. These potentials combine the accuracy of electronic structure calculations with the ability to reach extensive length and time scales. The i-PI package facilitates integrating the latest developments in this field with advanced modeling techniques thanks to a modular software architecture based on inter-process communication through a socket interface. The choice of Python for implementation facilitates rapid prototyping but can add computational overhead. In this new release, we carefully benchmarked and optimized i-PI for several common simulation scenarios, making such overhead negligible when i-PI is used to model systems up to tens of thousands of atoms using widely adopted machine learning interatomic potentials, such as Behler-Parinello, DeePMD, and MACE neural networks. We also present the implementation of several new features, including an efficient algorithm to model bosonic and fermionic exchange, a framework for uncertainty quantification to be used in conjunction with machine-learning potentials, a communication infrastructure that allows for deeper integration with electronic-driven simulations, and an approach to simulate coupled photon-nuclear dynamics in optical or plasmonic cavities.
在过去十年中，原子尺度模拟取得了巨大进展，这在很大程度上归功于机器学习原子间势的可用性。这些势将电子结构计算的准确性与达到广泛长度和时间尺度的能力结合起来。由于基于通过套接字接口进行进程间通信的模块化软件架构，i-PI软件包有助于将该领域的最新发展与先进的建模技术相结合。选择Python进行实现便于快速原型制作，但可能会增加计算开销。在这个新版本中，我们针对几种常见的模拟场景仔细地进行了基准测试并优化了i-PI，当使用广泛采用的机器学习原子间势（如Behler-Parinello、DeePMD和MACE神经网络）将i-PI用于对多达数万个原子的系统进行建模时，这种开销可以忽略不计。我们还展示了几个新功能的实现，包括一种用于模拟玻色子和费米子交换的高效算法、一个与机器学习势结合使用的不确定性量化框架、一种允许与电子驱动模拟进行更深入集成的通信基础设施，以及一种在光学或等离子体腔中模拟耦合光子-核动力学的方法。
