Machine Learning for Advanced Functional Materials

Joshi, Nirav, Kushvaha, Vinod, Madhushri, Priyanka

Machine Learning for Advanced Functional Materials
  • 出版商: Springer
  • 出版日期: 2024-05-24
  • 售價: $4,700
  • 貴賓價: 9.5 折 $4,465
  • 語言: 英文
  • 頁數: 303
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 9819903955
  • ISBN-13: 9789819903955
  • 相關分類: Machine Learning

    • 海外代購書籍(需單獨結帳)

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商品描述

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.

商品描述(中文翻譯)

本書介紹了機器學習方法在材料科學和納米技術中的最新進展及其應用。它為那些希望在材料特性建模和數據分析方面探索機器學習的人提供了一個入門。本書討論了基於可用的監督學習回歸方法和材料屬性優化的方法來提高材料的電氣和機械性能。總之，學術界和專業人士對於機器學習方法在功能性納米材料（如傳感器、太陽能電池和光催化）中的興趣日益增長，這是本書的推動力。本書是一本關於機器學習在先進功能材料中的綜合科學參考書，通過使用機器學習方法專注於熱點問題，深入探討了材料科學的最新成就。

作者簡介

Dr. Niravkumar J. Joshi is Physicist, having completed his doctorate at the Maharaja Sayajirao University of Baroda, India. He is Visiting Professor at Federal University of ABC, Brazil. He has postdoctoral experience from South Korea, Brazil, and at the University of California Berkeley, USA, where he developed selective and sensitive microsensors by MEMS techniques. His present research focuses on the synthesis and characterization of oxide nanostructures and 2D material-based gas sensors.

Dr. Vinod Kushvaha earned his Dual Degree (B. Tech. + M. Tech.) from the Indian Institute of Technology Bombay (IIT Bombay) in Civil Engineering (Specialization in Structural Engineering), following that he earned his second master's and a Ph.D. degree in Mechanical Engineering (focused on Fracture Characterization of Composite Materials under Impact Loading) at Auburn University, Auburn, AL, USA. Presently, Vinod is working at the Indian Institute of Technology Jammu (IIT Jammu) as Assistant Professor in the Civil Engineering department.
Dr. Priyanka Madhushri is Internet of Things (IoT) Ideation Research Engineer at Stanley Black and Decker (SBD), Atlanta. Priyanka obtained her Ph.D. in Electrical Engineering from University of Alabama in Huntsville, AL, USA. Currently, she works with the innovation team and brings new ideas to a variety of projects. As Researcher, she provides Proof of Concept (POC) to various SBD teams and assists in the development of company's software, hardware, and data analytics. Her research interests include the predictive analyses using machine learning, material modeling, Internet of things (IoT), mobile computing, etc. She has published in various engineering fields including materials journals where her work was focused on utilizing various machine learning algorithms to predict and explain mechanical behavior of advanced engineering materials.

作者簡介(中文翻譯)

Dr. Niravkumar J. Joshi是一位物理學家，他在印度巴羅達大學完成了博士學位。他現任巴西聯邦ABC大學的訪問教授。他在韓國、巴西和美國加州大學伯克利分校進行了博士後研究，並利用微機電系統技術開發了選擇性和敏感的微感測器。他目前的研究重點是合成和表徵氧化物納米結構和基於二維材料的氣體傳感器。

Dr. Vinod Kushvaha在印度孟買印度理工學院（IIT Bombay）獲得了雙學位（B. Tech. + M. Tech.）的土木工程學位（結構工程專業），之後在美國奧本大學（Auburn University）獲得了第二個碩士學位和博士學位（專注於復合材料在衝擊載荷下的斷裂特性）。目前，Vinod在印度印度理工學院賈姆穆（IIT Jammu）擔任土木工程系的助理教授。

Dr. Priyanka Madhushri是互聯網物聯網（IoT）創意研究工程師，就職於亞特蘭大的Stanley Black and Decker（SBD）公司。Priyanka在美國亞拉巴馬大學漢茨維爾分校（University of Alabama in Huntsville）獲得了電氣工程博士學位。目前，她與創新團隊合作，為各種項目帶來新的想法。作為研究人員，她為SBD的各個團隊提供概念驗證（POC），並協助開發公司的軟件、硬件和數據分析。她的研究興趣包括使用機器學習進行預測分析、材料建模、物聯網（IoT）、移動計算等。她在各種工程領域發表了論文，其中她的工作重點是利用各種機器學習算法預測和解釋先進工程材料的機械行為。

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