scikit-matter:一套可概括的机器学习方法,源于化学和材料科学
Alexander Goscinski1, Victor Paul Principe1, Guillaume Fraux1
1Laboratory of Computational Science and Modeling (COSMO), Institute of Materials, Ecole Polytechnique Federale de Lausanne, Lausanne, Vaud, 1015, Switzerland.
与scikit-learn兼容的scikit-matter Python库提供了化学和材料科学领域的域异性机器学习 (ML) 方法. 这促进了科学领域的更广泛采用和互操作性.
科学领域:
- 计算化学和材料科学 计算化学和材料科学
- 机器学习应用程序 机器学习应用程序
背景情况:
- 机器学习 (ML) 的采用被像scikit-learn.com这样的用户友好的库所加速.
- 机器学习算法虽然起源于特定领域,但显示出广泛的概括性.
- 化学和材料科学界在过去二十年中开发了通用ML方法.
研究的目的:
- 为了介绍scikit-matter,一个用于域异的ML方法的Python库.
- 为了更容易地将化学和材料科学ML方法融入其他领域.
- 确保与现有的ML工作流程的可用性和互操作性.
主要方法:
- 从化学和材料科学中开发了ML方法的领域无关实现.
- 坚持 scikit-learn API 和编码指南.
- 专注于通用性和跨领域部署的方便性.
主要成果:
- Scikit-matter提供了可访问的,通用的ML工具.
- 图书馆减少了将专业方法集成到各种工作流中的负担.
- 促进与现有的基于scikit-learn的工作流程的无互操作性.
结论:
- Scikit-matter促进了先进的ML技术的跨领域应用.
- 该图书馆增强了化学和材料科学中开发的方法的实用性和范围.
- 鼓励在科学研究中更广泛地采用数据驱动方法.
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