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.

中文翻译：

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材料科学家的机器学习：最佳实践入门指南

该方法/协议文章适用于对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，模型和体系结构共享以及最终发布的广泛准则和最佳实践。此外，我们还包括带有示例Python代码的交互式Jupyter笔记本，以演示所讨论的一些概念，工作流和最佳实践。总体而言，以简单的方式介绍了数据驱动的方法，机器学习工作流程和注意事项，使感兴趣的读者可以使用建议的参考资料，最佳做法，