dpdata:用于原子机器学习数据集的可扩展的Python工具包
Jinzhe Zeng1,2,3, Xingliang Peng4, Yong-Bin Zhuang5
1School of Artificial Intelligence and Data Science, Unversity of Science and Technology of China, Hefei 230026, P. R. China.
Journal of chemical information and modeling
|October 20, 2025
概括
机器学习潜力 (MLP) 的原子数据管理由dpdata,一个开源的Python库简化. 它简化了数据处理,转换和处理,提高了MLP开发的效率.
科学领域:
- 计算化学的计算化学
- 材料科学 材料科学 材料科学
- 机器学习 机器学习
背景情况:
- 对原子数据集的有效管理对于开发和部署机器学习潜力 (MLP) 至关重要.
- 现有的工具在处理MLP工作流中的各种数据格式和处理需求时可能效率低下.
研究的目的:
- 介绍dpdata,一个开源的Python库,旨在简化和优化MLP对原子数据的处理.
- 为阅读,写作,转换和处理各种数据集提供灵活和可扩展的解决方案.
主要方法:
- 开发了一个基于插件的架构,支持从量子化学和分子动力学软件的各种文件格式.
- 实现了数据预处理的关键实用程序,包括列车测试分割,坐标扰动,异常值删除和单位转换.
- 利用 NumPy 支持的高效存储和系统级操作来优化内存和速度.
主要成果:
- dpdata支持广泛的文件格式,并允许自定义扩展到新的软件.
- 与ASE等配置逐配置工具相比,实现了显著的内存节省和推断加快.
- 通过其在已发表的数据转换,存储和处理研究中的使用来证明其实际影响.
结论:
- dpdata为机器学习潜力开发中的原子数据管理提供了强大而高效的解决方案.
- 图书馆的灵活性和性能提升有助于简化工作流程,加快计算材料科学领域的研究.
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