<br>力场分析软件和工具 (FFAST)：在显微镜下评估机器学习力场,Journal of Chemical Theory and Computation

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力场分析软件和工具 (FFAST)：在显微镜下评估机器学习力场
Journal of Chemical Theory and Computation ( IF 5.7 ) Pub Date : 2023-11-27 , DOI: 10.1021/acs.jctc.3c00985
Gregory Fonseca ₁ , Igor Poltavsky ₁ , Alexandre Tkatchenko ₁

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随着机器学习力场 (MLFF) 的复杂性不断提高，以匹配扩展分子和材料的复杂性，正确分析和评估 MLFF 实际性能的工具的需求也随之增加。为了超越平均误差指标并全面了解模型的适用性和局限性，我们开发了 FFAST（力场分析软件和工具）：一个跨平台软件包，旨在详细了解模型的性能和局限性，并配有易于使用的图形用户界面。该软件允许用户测量任何分子力场的性能（例如流行的最先进的 MLFF 模型）在各种流行数据集类型上的性能，提供一般预测误差概述、异常值检测机制、原子投影错误等等。它有一个 3D 可视化工具，可以在大型数据集中查找和描绘有问题的配置、原子或簇。在本文中，MACE 和 NequIP 模型的示例用于两个感兴趣的数据集 [水苏糖和二十二碳六烯酸 (DHA)]，以说明该软件的用例。由此发现，水苏糖分子内糖苷键中或糖苷键附近的碳和氧会增加预测误差。此外，DHA 的预测误差随着分子折叠而增加，尤其是分子边缘的羧基。我们强调需要对 MLFF 模型进行系统评估，以确保其成功应用于分子和材料动力学研究。

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Force Field Analysis Software and Tools (FFAST): Assessing Machine Learning Force Fields under the Microscope

As the sophistication of machine learning force fields (MLFF) increases to match the complexity of extended molecules and materials, so does the need for tools to properly analyze and assess the practical performance of MLFFs. To go beyond average error metrics and into a complete picture of a model’s applicability and limitations, we developed FFAST (force field analysis software and tools): a cross-platform software package designed to gain detailed insights into a model’s performance and limitations, complete with an easy-to-use graphical user interface. The software allows the user to gauge the performance of any molecular force field,─such as popular state-of-the-art MLFF models, ─ on various popular data set types, providing general prediction error overviews, outlier detection mechanisms, atom-projected errors, and more. It has a 3D visualizer to find and picture problematic configurations, atoms, or clusters in a large data set. In this paper, the example of the MACE and NequIP models is used on two data sets of interest [stachyose and docosahexaenoic acid (DHA)]─to illustrate the use cases of the software. With this, it was found that carbons and oxygens involved in or near glycosidic bonds inside the stachyose molecule present increased prediction errors. In addition, prediction errors on DHA rise as the molecule folds, especially for the carboxylic group at the edge of the molecule. We emphasize the need for a systematic assessment of MLFF models for ensuring their successful application to the study of dynamics of molecules and materials.

更新日期：2023-11-27

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