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Machine Learning for Advanced Functional Materials

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. Th...

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Korporativní autor: SpringerLink (Online service)
Další autoři: Joshi, Nirav (Editor), Kushvaha, Vinod (Editor), Madhushri, Priyanka (Editor)
Médium: Elektronický zdroj E-kniha
Jazyk:angličtina
Vydáno: Singapore : Springer Nature Singapore : Imprint: Springer, 2023.
Vydání:1st ed. 2023.
Témata:
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.
On-line přístup:https://doi.org/10.1007/978-981-99-0393-1
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Obsah:
  • 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.

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