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：
10.26434/chemrxiv.12249752
复制
发表时间：
2020-05
影响因子：
8.6
通讯作者：
A. Wang;Ryan Murdock;Steven K. Kauwe;A. Oliynyk;A. Gurlo;Jakoah Brgoch;K. Persson;Taylor D. Sparks
中科院分区：
文献类型：
--
作者：
A. Wang;Ryan Murdock;Steven K. Kauwe;A. Oliynyk;A. Gurlo;Jakoah Brgoch;K. Persson;Taylor D. Sparks
本社论旨在为有兴趣进行以机器学习为中心的研究的材料科学家提供指导。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出版的广泛指导方针和最佳实践。此外，我们包括交互式的Python笔记本，其中包含示例Python代码，以演示所讨论的一些概念，工作流和最佳实践。总体而言，数据驱动方法和机器学习工作流和注意事项以简单的方式呈现，允许感兴趣的读者使用建议的参考资料、最佳实践和他们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 Editorial 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 datasets, 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.