Materials Informatics and Catalysts Informatics An Introduction

Readers will explore fundamental programming techniques, with a specific focus on Python, a versatile and widely-used language in the field. The textbook explores various machine learning techniques, equipping learners with the knowledge to harness the power of data science effectively. The textbook provides Python code examples, demonstrating materials informatics applications, and offers a deeper understanding through real-world case studies using materials and catalysts data. This practical exposure ensures readers are fully prepared to embark on their informatics-driven research endeavors upon completing the textbook. 

Instructors will also find immense value in this resource, as it consolidates the skills and information required for materials informatics into one comprehensive repository. This streamlines the course development process, significantly reducing the time spent on creating course material. Instructors can leverage this solid foundation to craft engaging and informative lecture content, making the teaching process more efficient and effective. 

Lauren Takahashi is an assistant professor at the Department of Chemistry at Hokkaido University in Japan. She is also a member of the Information Chemistry group with Professor Takahashi where she applies ontology and data science towards material design and catalyst-centered research. She has earned a B.A. in Linguistics at the University of Arizona, an M.S. in Communication at the University of Gothenburg, and a Ph.D. in Chemical Systems Engineering at The University of Tokyo. Her research interests focus on improving the structure, usability, and semantics of materials and catalyst data through tactical applications of ontology, data science, machine learning, graph theory, and information science, with the aim of improving the materials and catalysts design process.