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Lieferstatus:	i.d.R. innert 5-10 Tagen versandfertig
Veröffentlichung:	April 2026
Genre:	Naturwissensch., Medizin, Technik
	Cheminformatics / Design / machinelearning / Maschinelles Lernen / MaterialsInformatics / Materialwissenschaft / Predictivemodeling / q-RASPR / QSPR / VALIDATION mehr...
ISBN:	9783031787386
EAN-Code:	9783031787386
Verlag:	Springer
Einband:	Kartoniert
Sprache:	English
Dimensionen:	H 235 mm / B 155 mm / D 17 mm
Gewicht:	470 gr
Seiten:	308
Bewertung:	Titel bewerten / Meinung schreiben

Inhalt:

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.