Materials Informatics I
Methods
Series: Challenges and Advances in Computational Chemistry and Physics; 39;
- Publisher's listprice EUR 267.49
-
113 469 Ft (108 065 Ft + 5% VAT)
The price is estimated because at the time of ordering we do not know what conversion rates will apply to HUF / product currency when the book arrives. In case HUF is weaker, the price increases slightly, in case HUF is stronger, the price goes lower slightly.
- Discount 8% (cc. 9 078 Ft off)
- Discounted price 104 391 Ft (99 420 Ft + 5% VAT)
113 469 Ft
Availability
Not yet published.
Why don't you give exact delivery time?
Delivery time is estimated on our previous experiences. We give estimations only, because we order from outside Hungary, and the delivery time mainly depends on how quickly the publisher supplies the book. Faster or slower deliveries both happen, but we do our best to supply as quickly as possible.
Product details:
- Publisher Springer
- Date of Publication 3 April 2025
- Number of Volumes 1 pieces, Book
- ISBN 9783031787355
- Binding Hardback
- No. of pages288 pages
- Size 235x155 mm
- Language English
- Illustrations 13 Illustrations, black & white; 53 Illustrations, color 700
Categories
Short description:
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.
MoreLong description:
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.
MoreTable of Contents:
Part 1. Introduction.- Introduction to Materials Informatics.- Introduction to Cheminformatics for Predictive Modeling.- Introduction to machine learning for predictive modeling of organic materials.- Quantitative Structure-Property Relationships (QSPR) for Materials Science.- Part 2. Methods and Tools.- Quantitative Structure-Property Relationships (QSPR) and Machine Learning (ML) Models for Materials Science.- Optimising Materials Properties with Minimal Data: Lessons from Vanadium Catalyst Modelling.- In silico QSPR studies based on CDFT and IT descriptors.- Applications of quantitative read-across structure-property relationship (q-RASPR) modeling in the field of materials science.- Machine Learning algorithms for applications in Materials Science I.- Machine Learning algorithms for applications in Materials Science II.- Structure-property modeling of quantum-theoretic properties of benzenoid hydrocarbons by means of connection-related graphical descriptors.- Machine learning tools and Web services for Materials Science modelling.
More
