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Roy Kunal Banerjee Arkaprava (42 results)
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Condition: New.
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Condition: As New. Unread book in perfect condition.
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q-RASAR: A Path to Predictive Cheminformatics (SpringerBriefs in Molecular Science)
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
£ 45.35
£ 11.98 shipping
Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketCondition: New. In.
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Q-rasar : A Path to Predictive Cheminformatics
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: New.
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Q-rasar : A Path to Predictive Cheminformatics
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: As New. Unread book in perfect condition.
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Condition: New. 1st ed. 2024 edition NO-PA16APR2015-KAP.
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Paperback. Condition: Brand New. 101 pages. 9.25x6.10x0.22 inches. In Stock.
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Paperback. Condition: Brand New. 96 pages. 9.25x6.10x9.24 inches. In Stock.
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Condition: New.
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Hardcover. Condition: Brand New. 386 pages. 9.26x6.11x9.00 inches. In Stock.
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Materials Informatics III (Hardcover)
Language: English
Published by Springer International Publishing AG, Cham, 2025
ISBN 10: 3031787234 ISBN 13: 9783031787232
Hardcover. Condition: new. Hardcover. This contributed volume focuses on the application of machine learning and cheminformatics in predictive modeling for organic materials, polymers, solvents, and energetic materials. It provides an in-depth look at how machine learning is utilized to predict key properties of polymers, deep eutectic solvents, and ionic liquids, as well as to improve safety and performance in the study of energetic and reactive materials. With chapters covering polymer informatics, quantitative structureproperty relationship (QSPR) modeling, and computational approaches, the book serves as a comprehensive resource for researchers applying predictive modeling techniques to advance materials science and improve material safety and performance. This contributed volume focuses on the application of machine learning and cheminformatics in predictive modeling for organic materials, polymers, solvents, and energetic materials. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Materials Informatics III (Hardcover)
Language: English
Published by Springer International Publishing AG, Cham, 2025
ISBN 10: 3031787234 ISBN 13: 9783031787232
Hardcover. Condition: new. Hardcover. This contributed volume focuses on the application of machine learning and cheminformatics in predictive modeling for organic materials, polymers, solvents, and energetic materials. It provides an in-depth look at how machine learning is utilized to predict key properties of polymers, deep eutectic solvents, and ionic liquids, as well as to improve safety and performance in the study of energetic and reactive materials. With chapters covering polymer informatics, quantitative structureproperty relationship (QSPR) modeling, and computational approaches, the book serves as a comprehensive resource for researchers applying predictive modeling techniques to advance materials science and improve material safety and performance. This contributed volume focuses on the application of machine learning and cheminformatics in predictive modeling for organic materials, polymers, solvents, and energetic materials. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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Condition: As New. Unread book in perfect condition.
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Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This contributed volumeexplores the application of machine learning in predictive modeling within the fields of materials science, nanotechnology, and cheminformatics. It covers a range of topics, including electronic properties of metal nanoclusters, carbon quantum dots, toxicity assessments of nanomaterials, and predictive modeling for fullerenes and perovskite materials. Additionally, the book discusses multiscale modeling and advanced decision support systems for nanomaterial risk management, while also highlighting various machine learning tools, databases, and web platforms designed to predict the properties of materials and molecules. It is a comprehensive guide and a great tool for researchers working at the intersection of machine learning and material sciences.
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Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This contributed volumefocuses on the application of machine learning and cheminformatics in predictive modeling for organic materials, polymers, solvents, and energetic materials. It provides an in-depth look at how machine learning is utilized to predict key properties of polymers, deep eutectic solvents, and ionic liquids, as well as to improve safety and performance in the study of energetic and reactive materials. With chapters covering polymer informatics, quantitative structure property relationship (QSPR) modeling, and computational approaches, the book serves as a comprehensive resource for researchers applying predictive modeling techniques to advance materials science and improve material safety and performance.
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Hardcover. Condition: Brand New. 386 pages. 9.26x6.11x9.00 inches. In Stock.
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Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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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Materials Informatics III (Hardcover)
Language: English
Published by Springer International Publishing AG, Cham, 2025
ISBN 10: 3031787234 ISBN 13: 9783031787232
Hardcover. Condition: new. Hardcover. This contributed volume focuses on the application of machine learning and cheminformatics in predictive modeling for organic materials, polymers, solvents, and energetic materials. It provides an in-depth look at how machine learning is utilized to predict key properties of polymers, deep eutectic solvents, and ionic liquids, as well as to improve safety and performance in the study of energetic and reactive materials. With chapters covering polymer informatics, quantitative structureproperty relationship (QSPR) modeling, and computational approaches, the book serves as a comprehensive resource for researchers applying predictive modeling techniques to advance materials science and improve material safety and performance. This contributed volume focuses on the application of machine learning and cheminformatics in predictive modeling for organic materials, polymers, solvents, and energetic materials. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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Condition: New. Print on Demand.
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Condition: New. PRINT ON DEMAND.
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Condition: New. Print on Demand.
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q-RASAR
Language: English
Published by Springer Nature Switzerland, 2024
ISBN 10: 3031520564 ISBN 13: 9783031520563
Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Introduces the reader to a novel cheminformatic workflowPresents the genesis and model developmentIncludes practical examples and software toolsDr. Kunal Roy is a Professor & Ex-Head in the Department of Pharmaceutical Tec.
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Condition: New. PRINT ON DEMAND.
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Activity Cliffs
Language: English
Published by Springer Verlag GmbH, 2026
ISBN 10: 3032100801 ISBN 13: 9783032100801
Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt.
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Condition: new. Questo è un articolo print on demand.
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Condition: new. Questo è un articolo print on demand.
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Materials Informatics III
Language: English
Published by Springer Verlag GmbH, 2025
ISBN 10: 3031787234 ISBN 13: 9783031787232
Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt.
