04418nam a22005775i 4500 978-981-97-0217-6 DE-He213 20251030111756.0 cr nn 008mamaa 240330s2024 si | s |||| 0|eng d 9789819702176 978-981-97-0217-6 10.1007/978-981-97-0217-6 doi TA404.23 TGM bicssc TEC021000 bisacsh TGM thema Takahashi, Keisuke. author. (orcid)0000-0002-9328-1694 https://orcid.org/0000-0002-9328-1694 aut http://id.loc.gov/vocabulary/relators/aut 74458 Materials Informatics and Catalysts Informatics [electronic resource] : An Introduction / by Keisuke Takahashi, Lauren Takahashi. 1st ed. 2024. Singapore : Springer Nature Singapore : Imprint: Springer, 2024. IX, 297 p. 1 illus. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Chapter 1. An Introduction to Materials Informatics and Catalysts Informatics -- Chapter 2. Developing an Informatics Work Environment -- Chapter 3. Programming -- Chapter 4. Programming and Python -- Chapter 5. Data and Materials and Catalysts Informatics -- Chapter 6. Data Visualization -- Chapter 7. Machine Learning -- Chapter 8. Supervised Machine Learning -- Chapter 9. Unsupervised Machine Learning and Beyond Machine Learning. This textbook is designed for students and researchers who are interested in materials and catalysts informatics with little to no prior experience in data science or programming languages. Starting with a comprehensive overview of the concept and historical context of materials and catalysts informatics, it serves as a guide for establishing a robust materials informatics environment. This essential resource is designed to teach vital skills and techniques required for conducting informatics-driven research, including the intersection of hardware, software, programming, machine learning within the field of data science and informatics. 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. . Materials science Data processing. 67985 Chemoinformatics. 70652 Catalysis. 66843 Chemistry Data processing. 69913 Graph theory. Computational Materials Science. 67987 Cheminformatics. 65190 Catalysis. 66843 Computational Chemistry. 69915 Graph Theory. Takahashi, Lauren. author. (orcid)0000-0001-9922-8889 https://orcid.org/0000-0001-9922-8889 aut http://id.loc.gov/vocabulary/relators/aut 74459 SpringerLink (Online service) Springer Nature eBook Printed edition: 9789819702169 Printed edition: 9789819702183 Printed edition: 9789819702190 https://doi.org/10.1007/978-981-97-0217-6 ZDB-2-CMS ZDB-2-SXC 58834 58834