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Materials informatics.. II,. Softwar...

Roy, Kunal.

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Materials informatics.. II,. Software tools and databases

Record Type:	Electronic resources : Monograph/item
Title/Author:	Materials informatics./ edited by Kunal Roy, Arkaprava Banerjee.
remainder title:	Software tools and databases
other author:	Roy, Kunal.
Published:	Cham :Springer Nature Switzerland : : 2025.,
Description:	xvi, 297 p. :ill., digital ;24 cm.
[NT 15003449]:	Part 1. Introduction -- Introduction to Machine Learning for Predictive Modeling I -- Introduction to Machine Learning for Materials Property Modeling -- Part 2. Cheminformatic and Machine Learning Models for Nanomaterials -- Machine learning models to study electronic properties of metal nanoclusters -- Applications of Machine Learning Predictive Modeling for Carbon Quantum Dots -- Assessing the toxicity of quantum dots in healthy and tumoral cells with ProtoNANO, a platform of nano-QSAR models to predict the toxicity of inorganic nanomaterials -- Applications of predictive modeling for fullerenes -- Computational Analysis of Perovskite Materials AlXY3 (X = Cu, Mn; Y = Br, Cl, F) invoking the DFT Method -- Applications of predictive modeling for dye-sensitized solar cells (DSSCs) -- Introduction to multiscale modeling for One Health approaches -- DIAGONAL Decision Support System (DSS) for Advanced Nanomaterial Risk Management powered by Enalos Cloud Platform -- Part 3. Software Tools and Databases for Applications in Materials Science -- Machine Learning algorithms, tools, and databases for applications in Materials Science -- Machine Learning-Driven Web Tools for Predicting Properties of Materials and Molecules.
Contained By:	Springer Nature eBook
Subject:	Nanostructured materials - Data processing. -
Online resource:	https://doi.org/10.1007/978-3-031-78728-7
ISBN:	9783031787287

Materials informatics.. II,. Software tools and databases
Materials informatics.II,Software tools and databases[electronic resource] /Software tools and databasesedited by Kunal Roy, Arkaprava Banerjee. - Cham :Springer Nature Switzerland :2025. - xvi, 297 p. :ill., digital ;24 cm. - Challenges and advances in computational chemistry and physics,v. 402542-4483 ;. - Challenges and advances in computational chemistry and physics ;v. 40..

Part 1. Introduction -- Introduction to Machine Learning for Predictive Modeling I -- Introduction to Machine Learning for Materials Property Modeling -- Part 2. Cheminformatic and Machine Learning Models for Nanomaterials -- Machine learning models to study electronic properties of metal nanoclusters -- Applications of Machine Learning Predictive Modeling for Carbon Quantum Dots -- Assessing the toxicity of quantum dots in healthy and tumoral cells with ProtoNANO, a platform of nano-QSAR models to predict the toxicity of inorganic nanomaterials -- Applications of predictive modeling for fullerenes -- Computational Analysis of Perovskite Materials AlXY3 (X = Cu, Mn; Y = Br, Cl, F) invoking the DFT Method -- Applications of predictive modeling for dye-sensitized solar cells (DSSCs) -- Introduction to multiscale modeling for One Health approaches -- DIAGONAL Decision Support System (DSS) for Advanced Nanomaterial Risk Management powered by Enalos Cloud Platform -- Part 3. Software Tools and Databases for Applications in Materials Science -- Machine Learning algorithms, tools, and databases for applications in Materials Science -- Machine Learning-Driven Web Tools for Predicting Properties of Materials and Molecules.

This contributed volume explores 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.

ISBN: 9783031787287

Standard No.: 10.1007/978-3-031-78728-7doiSubjects--Topical Terms:

2021778
Nanostructured materials
--Data processing.

LC Class. No.: TA418.9.N35

Dewey Class. No.: 620.115

Materials informatics.. II,. Software tools and databases
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260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2025.
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505 0 $a Part 1. Introduction -- Introduction to Machine Learning for Predictive Modeling I -- Introduction to Machine Learning for Materials Property Modeling -- Part 2. Cheminformatic and Machine Learning Models for Nanomaterials -- Machine learning models to study electronic properties of metal nanoclusters -- Applications of Machine Learning Predictive Modeling for Carbon Quantum Dots -- Assessing the toxicity of quantum dots in healthy and tumoral cells with ProtoNANO, a platform of nano-QSAR models to predict the toxicity of inorganic nanomaterials -- Applications of predictive modeling for fullerenes -- Computational Analysis of Perovskite Materials AlXY3 (X = Cu, Mn; Y = Br, Cl, F) invoking the DFT Method -- Applications of predictive modeling for dye-sensitized solar cells (DSSCs) -- Introduction to multiscale modeling for One Health approaches -- DIAGONAL Decision Support System (DSS) for Advanced Nanomaterial Risk Management powered by Enalos Cloud Platform -- Part 3. Software Tools and Databases for Applications in Materials Science -- Machine Learning algorithms, tools, and databases for applications in Materials Science -- Machine Learning-Driven Web Tools for Predicting Properties of Materials and Molecules.
520 $a This contributed volume explores 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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650 2 4 $a Artificial Intelligence. $3 769149
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