國立虎尾科技大學 |

Materials informatics.. II,. Software tools and databases

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Materials informatics./ edited by Kunal Roy, Arkaprava Banerjee.
其他題名:	Software tools and databases
其他作者:	Banerjee, Arkaprava.
出版者:	Cham :Springer Nature Switzerland : : 2025.,
面頁冊數:	xvi, 297 p. :ill., digital ; : 24 cm.;
Contained By:	Springer Nature eBook
標題:	Artificial Intelligence. -
電子資源:	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.14..

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:

646849
Artificial Intelligence.

LC Class. No.: TA418.9.N35

Dewey Class. No.: 620.115

Materials informatics.. II,. Software tools and databases
LDR:03171nam a2200349 a 4500 001 1160566
003 DE-He213
005 20250314115303.0
006 m d
007 cr nn 008maaau
008 251029s2025 sz s 0 eng d
020 $a 9783031787287 $q (electronic bk.)
020 $a 9783031787270 $q (paper)
024 7 $a 10.1007/978-3-031-78728-7 $2 doi
035 $a 978-3-031-78728-7
040 $a GP $c GP
041 0 $a eng
050 4 $a TA418.9.N35
072 7 $a PNR $2 bicssc
072 7 $a SCI013070 $2 bisacsh
072 7 $a PNRA $2 thema
082 0 4 $a 620.115 $2 23
090 $a TA418.9.N35 $b M425 2025
245 0 0 $a Materials informatics. $n II, $p Software tools and databases $h [electronic resource] / $c edited by Kunal Roy, Arkaprava Banerjee.
246 3 0 $a Software tools and databases
260 $a Cham : $c 2025. $b Springer Nature Switzerland : $b Imprint: Springer,
300 $a xvi, 297 p. : $b ill., digital ; $c 24 cm.
490 1 $a Challenges and advances in computational chemistry and physics, $x 2542-4483 ; $v v. 40
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.
650 2 4 $a Artificial Intelligence. $3 646849
650 2 4 $a Machine Learning. $3 1137723
650 2 4 $a Computational Design Of Materials. $3 1401354
650 0 $a Cheminformatics. $3 805435
650 0 $a Nanostructured materials $x Data processing. $3 1487642
700 1 $a Banerjee, Arkaprava. $3 1487629
700 1 $a Roy, Kunal. $3 1142239
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
830 0 $a Challenges and advances in computational chemistry and physics ; $v v.14. $3 1022973
856 4 0 $u https://doi.org/10.1007/978-3-031-78728-7
950 $a Chemistry and Materials Science (SpringerNature-11644)