Atom-centered machine-learning force field package
Atom-centered machine-learning force field package
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DOI：
10.1016/j.cpc.2023.108883
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发表时间：
2023-08
期刊：
影响因子：
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通讯作者：
Lei Li;Ryan A. Ciufo;Jiyoung Lee;Chuan Zhou-;Bo Lin;Jaeyoung Cho;Naman Katyal;G. Henkelman
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文献类型：
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作者：
Lei Li;Ryan A. Ciufo;Jiyoung Lee;Chuan Zhou-;Bo Lin;Jaeyoung Cho;Naman Katyal;G. Henkelman
近年来，机器学习算法已被广泛用于构建具有从头算方法的精度和经典力场的效率的力场。在这里，我们开发了一个基于Python的原子为中心的机器学习力场（PyAMFF）包，通过实现以Behler-Parrinello对称函数为结构指纹的原子为中心的神经网络算法，为拟合和使用机器学习力场提供一个简单有效的平台。PyAMFF具有以下三个特点：（1）集成了Fortran模块，用于快速指纹计算;集成了Python模块，通过脚本和对未来算法的方便扩展，实现了用户友好的集成;（2）一个纯Fortran后端与软件接口，包括长期动态模拟包EON，利用机器学习力场实现分子动力学模拟和自适应动力学蒙特卡罗模拟;以及（3）与用于主动学习和基于ML的算法开发的Atomic Simulation Environment包集成。在这里，我们展示了PyAMFF在CPU和内存使用方面的高效并行化，并表明基于Fortran的PyAMFF计算器表现出与对称函数数量和系统大小的线性缩放关系。程序摘要程序标题：python-based atom-centered machine-learning force field（PyAMFF）CPC Library程序文件链接：https：//doi.org/10.17632/fsn6dkcvrv.1Developer's repository link：https://gitlab.com/pyamff/pyamffLicensing条款：Apache License，2.0问题性质：根据密度泛函理论（DFT）的原子力和能量确定近似（替代）模型。使用替代模型，可以快速探索势能面，加速最小值和鞍点的优化，并最终加速活性材料的设计，其中动力学是材料功能的关键。解决方法：根据Behler-Parrinello指纹计算训练数据的原子环境。这些指纹被传递到一个神经网络，该神经网络被训练以再现训练数据的能量和力量。并行实现和Fortran后端允许有效的训练和计算所得到的代理模型。替代模型表面上的材料的长时间模拟的例子。
In recent years, machine learning algorithms have been widely used for constructing force fields with an accuracy of ab initio methods and the efficiency of classical force fields. Here, we developed a python-based atom-centered machine-learning force field (PyAMFF) package to provide a simple and efficient platform for fitting and using machine learning force fields by implementing an atom-centered neural-network algorithm with Behler-Parrinello symmetry functions as structural fingerprints. The following three features are included in PyAMFF: (1) integrated Fortran modules for fast fingerprint calculations and Python modules for user-friendly integration through scripts and facile extension of future algorithms; (2) a pure Fortran backend to interface with the software, including the long-timescale dynamic simulation package EON, enabling both molecular dynamic simulations and adaptive kinetic Monte Carlo simulations with machine-learning force fields; and (3) integration with the Atomic Simulation Environment package for active learning and ML-based algorithm development. Here, we demonstrate an efficient parallelization of PyAMFF in terms of CPU and memory usage and show that the Fortran-based PyAMFF calculator exhibits a linear scaling relationship with the number of symmetry functions and the system size.Program summaryProgram title:python-based atom-centered machine-learning force field (PyAMFF)CPC Library link to program files:https://doi.org/10.17632/fsn6dkcvrv.1Developer's repository link:https://gitlab.com/pyamff/pyamffLicensing provisions:Apache License, 2.0Nature of problem:Determine an approximate (surrogate) model based upon atomic forces and energies from density functional theory (DFT). With a surrogate model that is less computationally expensive to evaluate than DFT, there can be a rapid exploration of the potential energy surface, accelerated optimization to minima and saddle points, and ultimately, accelerated design of active materials where the kinetics are key to the material function.Solution method:The atomic environments of training data are calculated in terms of Behler-Parrinello fingerprints. These fingerprints are passed to a neural network which is trained to reproduce the energy and force of the training data. A parallel implementation and Fortran backend allow for efficient training and calculation of the resulting surrogate model. Examples of long-time simulations of materials on the surrogate model surfaces are provided.