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Materials Informatics II Software Tools and Databases /

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 na...

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Ente Autore: SpringerLink (Online service)
Altri autori: Roy, Kunal (Redattore), Banerjee, Arkaprava (Redattore)
Natura: Elettronico eBook
Lingua:inglese
Pubblicazione: Cham : Springer Nature Switzerland : Imprint: Springer, 2025.
Edizione:1st ed. 2025.
Serie:Challenges and Advances in Computational Chemistry and Physics, 40
Soggetti:
Chemoinformatics.
Materials.
Chemistry.
Computer simulation.
Machine learning.
Artificial intelligence.
Cheminformatics.
Computational Design Of Materials.
Machine Learning.
Artificial Intelligence.
Accesso online:https://doi.org/10.1007/978-3-031-78728-7
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Sommario:
  • 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.

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