Machine Learning for Materials Scientists: An Introductory Guide toward Best Practices
Machine Learning for Materials Scientists: An Introductory Guide toward Best Practices
复制标题
DOI：
10.1021/acs.chemmater.0c01907
复制
发表时间：
2020-06-23
影响因子：
8.6
通讯作者：
Sparks, Taylor D.
中科院分区：
文献类型：
--
作者：
Wang, Anthony Yu-Tung;Murdock, Ryan J.;Sparks, Taylor D.
这篇方法/协议文章适用于对执行以机器学习为中心的研究感兴趣的材料科学家。我们涵盖了有关数据获取和处理、特征工程、模型训练、验证、评估和比较、材料数据和基准数据集的流行存储库、模型和架构共享以及最终发布的广泛指导方针和最佳实践。此外，我们还提供了带有Python代码示例的交互式Linux笔记本，以演示所讨论的一些概念、工作流和最佳实践。总的来说，数据驱动的方法和机器学习工作流程和注意事项以简单的方式呈现，使感兴趣的读者能够使用建议的参考资料，最佳实践和自己的材料领域专业知识更智能地指导他们的机器学习研究。
This Methods/Protocols article is intended for materials scientists interested in performing machine learning-centered research. We cover broad guidelines and best practices regarding the obtaining and treatment of data, feature engineering, model training, validation, evaluation and comparison, popular repositories for materials data and benchmarking data sets, model and architecture sharing, and finally publication. In addition, we include interactive Jupyter notebooks with example Python code to demonstrate some of the concepts, workflows, and best practices discussed. Overall, the data-driven methods and machine learning workflows and considerations are presented in a simple way, allowing interested readers to more intelligently guide their machine learning research using the suggested references, best practices, and their own materials domain expertise.