We present ichor, an open-source Python library that simplifies data management in computational chemistry and streamlines machine learning force field development. Ichor implements many easily extensible file management tools, in addition to a lazy file reading system, allowing efficient management of hundreds of thousands of computational chemistry files. Data from calculations can be readily stored into databases for easy sharing and post-processing. Raw data can be directly processed by ichor to create machine learning-ready datasets. In addition to powerful data-related capabilities, ichor provides interfaces to popular workload management software employed by High Performance Computing clusters, making for effortless submission of thousands of separate calculations with only a single line of Python code. Furthermore, a simple-to-use command line interface has been implemented through a series of menu systems to further increase accessibility and efficiency of common important ichor tasks. Finally, ichor implements general tools for visualization and analysis of datasets and tools for measuring machine-learning model quality both on test set data and in simulations. With the current functionalities, ichor can serve as an end-to-end data procurement, data management, and analysis solution for machine-learning force-field development.

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Ichor：用于计算化学数据管理和机器学习力场开发的 Python 库


我们介绍了 ichor，这是一个开源 Python 库，可简化计算化学中的数据管理并简化机器学习力场开发。Ichor 实现了许多易于扩展的文件管理工具，以及一个懒惰的文件读取系统，允许高效管理数十万个计算化学文件。计算数据可以很容易地存储到数据库中，以便于共享和后处理。Ichor 可以直接处理原始数据，以创建可用于机器学习的数据集。除了强大的数据相关功能外，ichor 还为高性能计算集群使用的常用工作负载管理软件提供接口，只需一行 Python 代码即可轻松提交数千个单独的计算。此外，通过一系列菜单系统实现了一个易于使用的命令行界面，以进一步提高常见重要 ichor 任务的可访问性和效率。最后，ichor 实现了用于数据集可视化和分析的通用工具，以及用于在测试集数据和模拟中测量机器学习模型质量的工具。凭借当前功能，ichor 可以用作机器学习力场开发的端到端数据采购、数据管理和分析解决方案。