Deep learning

Architecture

Business

Feature engineering

Python (programming language)

Software engineering

Machine learning

Visual arts

Marketing

Workflow

Best practice

Artificial intelligence

Management

Economics

Programming language

Data science

Computer science

Database

Benchmarking

Chemistry of Materials

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.

Machine Learning for Materials Scientists: An Introductory Guide toward Best Practices