Materials informatics software tools (3 Ergebnisse)
- Softcover
Anbieter: preigu, Osnabrück, Deutschlandpreigu
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EUR 202,85
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Taschenbuch. Zustand: Neu. Materials Informatics II | Software Tools and Databases | Kunal Roy (u. a.) | Taschenbuch | Challenges and Advances in Computational Chemistry and Physics | xvi | Englisch | 2026 | Springer | EAN 9783031787300 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelber…g, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
- Hardcover
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EUR 235,39
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Buch. Zustand: 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, carbo…n 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.
- Softcover
Anbieter: AHA-BUCH GmbH, Einbeck, DeutschlandAHA-BUCH GmbH
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Neu
EUR 244,86
EUR 62,41 VersandVersand von Deutschland nach USAAnzahl: 1 verfügbar
Taschenbuch. Zustand: 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.
