Machine Learning for Materials Discovery: Numerical Recipes and Practical Applications (材料發現的機器學習：數值方法與實用應用)

Krishnan, N. M. Anoop, Kodamana, Hariprasad, Bhattoo, Ravinder

Machine Learning for Materials Discovery: Numerical Recipes and Practical Applications
  • 出版商: Springer
  • 出版日期: 2024-05-07
  • 售價: $6,140
  • 貴賓價: 9.5 折 $5,833
  • 語言: 英文
  • 頁數: 279
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 3031446216
  • ISBN-13: 9783031446214
  • 相關分類: Machine Learning

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商品描述

Focusing on the fundamentals of machine learning, this book covers broad areas of data-driven modeling, ranging from simple regression to advanced machine learning and optimization methods for applications in materials modeling and discovery. The book explains complex mathematical concepts in a lucid manner to ensure that readers from different materials domains are able to use these techniques successfully. A unique feature of this book is its hands-on aspect--each method presented herein is accompanied by a code that implements the method in open-source platforms such as Python. This book is thus aimed at graduate students, researchers, and engineers to enable the use of data-driven methods for understanding and accelerating the discovery of novel materials.


商品描述(中文翻譯)

本書專注於機器學習的基礎知識，涵蓋了從簡單回歸到高級機器學習和優化方法的廣泛資料驅動建模領域，適用於材料建模和探索的應用。本書以清晰易懂的方式解釋了複雜的數學概念，以確保來自不同材料領域的讀者能夠成功應用這些技術。本書的獨特之處在於實踐性，每種方法都附帶了在Python等開源平台上實現該方法的程式碼。因此，本書旨在幫助研究生、研究人員和工程師們使用數據驅動方法來理解和加速對新材料的發現。

作者簡介

N. M. Anoop Krishnan is an Associate Professor in the Department of Civil Engineering, IIT Delhi, with a joint affiliation in the Yardi School of Artificial Intelligence, IIT Delhi. Prior to this, he worked as Lecturer and Postdoctoral Researcher at the University of California, Los Angeles. His primary area of research includes data- and physics-based modeling of materials. He has published more than 100 peer-reviewed publications and won several prestigious awards including the Google research scholar award (2023), W. A. Weyl international glass science award, Young Associate 2022 (Indian Academy of Sciences), Young Engineer Award 2020 (Indian National Academy of Engineering).

Hariprasad Kodamana is an Associate Professor in the Department of Chemical Engineering, IIT Delhi withaffiliation in the Yardi School of Artificial Intelligence, IIT Delhi. Prior to this, he worked as Assistant Professor at IIT Kharagpur, Postdoctoral Researcher and Sessional Instructor at the University of Alberta, Canada, and Process Engineer at GE Energy. His primary area of research includes data-driven modeling and optimization. He serves as Reviewer for various scientific journals and has won several awards including the Young Faculty Incentive Fellowship (IIT Delhi) and the IIT Bombay Institute Award for best Ph.D. thesis.

Ravinder Bhattoo is currently a postdoctoral researcher in the University of Wisconsin-Madison. Prior to this, he completed his Ph.D. in the Department of Civil Engineering, IIT Delhi and undergraduate degree in civil engineering from IIT Roorkee. He works in the area of machine learning applied to glass science to predict the composition-property relationships in glasses. He has won several awards including the prestigious prime minister's research fellowship (PMRF).

作者簡介(中文翻譯)

N. M. Anoop Krishnan是印度德里印度理工學院土木工程系的副教授，同時也在印度德里印度理工學院Yardi人工智慧學院擔任職位。在此之前，他曾在加利福尼亞大學洛杉磯分校擔任講師和博士後研究員。他的主要研究領域包括基於數據和物理的材料建模。他發表了100多篇同行評審的論文，並獲得了多個重要獎項，包括Google研究學者獎（2023年）、W. A. Weyl國際玻璃科學獎、2022年青年聯絡人（印度科學院）、2020年青年工程師獎（印度國家工程學院）。

Hariprasad Kodamana是印度德里印度理工學院化學工程系的副教授，同時也在印度德里印度理工學院Yardi人工智慧學院擔任職位。在此之前，他曾在印度理工學院卡拉格普爾分校擔任助理教授，並在加拿大阿爾伯塔大學擔任博士後研究員和臨時講師，還曾在GE能源擔任工藝工程師。他的主要研究領域包括基於數據的建模和優化。他擔任多個科學期刊的審稿人，並獲得了多個獎項，包括印度德里印度理工學院的青年教師激勵獎學金和印度理工學院孟買分校最佳博士論文獎。

Ravinder Bhattoo目前是威斯康辛大學麥迪遜分校的博士後研究員。在此之前，他在印度德里印度理工學院土木工程系完成了博士學位，並在印度理工學院魯爾基分校獲得了土木工程學士學位。他的研究領域是應用於玻璃科學的機器學習，用於預測玻璃的組成-性能關係。他獲得了多個獎項，包括著名的總理研究獎學金（PMRF）。

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