Materials Informatics I: Methods (Challenges and Advances in Computational Chemistry and Physics #39)

By: Kunal Roy and Arkaprava Banerjee

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Synopsis: 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.

Book Quality:: Publisher Quality
ISBN-13:: 9783031787362
Related ISBNs:: 9783031787355
Publisher:: Springer Nature Switzerland
Date of Addition:: 05/10/25
Copyrighted By:: The Editor
Adult content:: No
Language:: English
Has Image Descriptions:: No
Categories:: Nonfiction, Science, Computers and Internet, Technology, Mathematics and Statistics
Submitted By:: Bookshare Staff
Usage Restrictions:: This is a copyrighted book.
Edited by:: Kunal Roy
Edited by:: Arkaprava Banerjee