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Machine Learning for Advanced Functional Materials / / edited by Nirav Joshi, Vinod Kushvaha, Priyanka Madhushri



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Autore: Joshi Nirav Visualizza persona
Titolo: Machine Learning for Advanced Functional Materials / / edited by Nirav Joshi, Vinod Kushvaha, Priyanka Madhushri Visualizza cluster
Pubblicazione: Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023
Edizione: 1st ed. 2023.
Descrizione fisica: 1 online resource (306 pages)
Disciplina: 620.110285631
Soggetto topico: Optics
Machine learning
Materials
Detectors
Tumor markers
Photonics
Optical engineering
Optics and Photonics
Machine Learning
Sensors and biosensors
Tumour Biomarkers
Photonics and Optical Engineering
Photonic Devices
Soggetto non controllato: Artificial Intelligence
Materials
Oncology
Microwaves
Optics
Computers
Technology & Engineering
Medical
Science
Altri autori: KushvahaVinod  
MadhushriPriyanka  
Nota di contenuto: Solar Cells and Relevant Machine Learning -- Machine learning-driven gas identification in gas sensors -- Recent advances in Machine Learning for electrochemical, optical, and gas sensors -- Machine Learning in Wearable Healthcare Devices -- A Machine Learning approach in wearable Technologies -- The application of novel functional materials to machine learning -- Potential of Machine Learning Algorithms in Material Science: Predictions in design, properties and applications of novel functional materials -- Perovskite Based Materials for Photovoltaic Applications: A Machine Learning Approach -- A review of the high-performance gas sensors using machine learning -- Machine Learning For Next‐Generation Functional Materials -- Contemplation of Photocatalysis Through Machine Learning -- Discovery of Novel Photocatalysts using Machine Learning Approach -- Machine Learning In Impedance Based Sensors.
Sommario/riassunto: This book presents recent advancements of machine learning methods and their applications in material science and nanotechnologies. It provides an introduction to the field and for those who wish to explore machine learning in modeling as well as conduct data analyses of material characteristics. The book discusses ways to enhance the material’s electrical and mechanical properties based on available regression methods for supervised learning and optimization of material attributes. In summary, the growing interest among academics and professionals in the field of machine learning methods in functional nanomaterials such as sensors, solar cells, and photocatalysis is the driving force for behind this book. This is a comprehensive scientific reference book on machine learning for advanced functional materials and provides an in-depth examination of recent achievements in material science by focusing on topical issues using machine learning methods.
Titolo autorizzato: Machine Learning for Advanced Functional Materials  Visualizza cluster
ISBN: 981-9903-93-9
Formato: Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione: Inglese
Record Nr.: 9910726286403321
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