Materials Informatics I
Methods
Springer International Publishing
ISBN 978-3-031-78736-2
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Bibliografische Daten
eBook. PDF
2025
XVII, 288 p. 66 illus., 53 illus. in color..
In englischer Sprache
Umfang: 288 S.
Verlag: Springer International Publishing
ISBN: 978-3-031-78736-2
Weiterführende bibliografische Daten
Das Werk ist Teil der Reihe: Challenges and Advances in Computational Chemistry and Physics
Produktbeschreibung
This contributed volume explores the integration of machine learning and cheminformatics within materials science, focusing on predictive modeling techniques. It begins with foundational concepts in materials informatics and cheminformatics, emphasizing quantitative structure-property relationships (QSPR). The volume then presents various methods and tools, including advanced QSPR models, quantitative read-across structure-property relationship (q-RASPR) models, optimization strategies with minimal data, and in silico studies using different descriptors. Additionally, it explores machine learning algorithms and their applications in materials science, alongside innovative modeling approaches for quantum-theoretic properties. Overall, the book serves as a comprehensive resource for understanding and applying machine learning in the study and development of advanced materials and is a useful tool for students, researchers and professionals working in these areas.
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