{"product_id":"machine-learning-in-2d-materials-science-paperback","title":"Machine Learning in 2D Materials Science - Paperback","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eParvathi Chundi\u003c\/b\u003e (Editor), \u003cb\u003eVenkataramana Gadhamshetty\u003c\/b\u003e (Editor), \u003cb\u003eBharat K. Jasthi\u003c\/b\u003e (Editor)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eData science and machine learning (ML) methods are increasingly being used to transform the way research is being conducted in materials science to enable new discoveries and design new materials. For any materials science researcher or student, it may be daunting to figure out if ML techniques are useful for them or, if so, which ones are applicable in their individual contexts, and how to study the effectiveness of these methods systematically.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eKEY FEATURES\u003c\/strong\u003e\u003c\/p\u003e\u003cul\u003e \u003cli\u003eProvides broad coverage of data science and ML fundamentals to materials science researchers so that they can confidently leverage these techniques in their research projects\u003c\/li\u003e \u003cli\u003eOffers introductory material in topics such as ML, data integration, and 2D materials\u003c\/li\u003e \u003cli\u003eProvides in-depth coverage of current ML methods for validating 2D materials using both experimental and simulation data, researching and discovering new 2D materials, and enhancing ML methods with physical properties of materials\u003c\/li\u003e \u003cli\u003eDiscusses customized ML methods for 2D materials data and applications and high-throughput data acquisition\u003c\/li\u003e \u003cli\u003eDescribes several case studies illustrating how ML approaches are currently leading innovations in the discovery, development, manufacturing, and deployment of 2D materials needed for strengthening industrial products\u003c\/li\u003e \u003cli\u003eGives future trends in ML for 2D materials, explainable AI, and dealing with extremely large and small, diverse datasets\u003c\/li\u003e \u003c\/ul\u003e\u003cp\u003eAimed at materials science researchers, this book allows readers to quickly, yet thoroughly, learn the ML and AI concepts needed to ascertain the applicability of ML methods in their research.\u003c\/p\u003e\u003ch3\u003eAuthor Biography\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eParvathi Chundi\u003c\/strong\u003e, PhD is Professor of Computer Science, University of Nebraska-Omaha. Prior to Omaha, Dr. Chundi was with Agilent Technologies and HP Labs, both in Palo Alto, CA.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eVenkataramana Gadhamshetty\u003c\/strong\u003e, PhD, PE is Professor of Environmental Engineering in Department of Civil and Environmental Engineering, South Dakota School of Mines and Technology. He is a cofounder of 2D materials for Biofilm Science Engineering and Technology (2DBEST) center and 2D materials laboratory (2DML) at SDSM\u0026amp;T.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eBharat K. Jasthi\u003c\/strong\u003e, PhD is Associate Professor, Department of Materials and Metallurgical Engineering, South Dakota School of Mines and Technology. Dr. Jasthi has research expertise in the areas of microstructural modification, structure property correlation, new alloy development, powder metallurgy, additive manufacturing, and development of engineered surface thin films and coatings for a wide range of applications.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eCarol Lushbough\u003c\/strong\u003e, MA is an Emeritus Professor of Computer Science, University of South Dakota.\u003c\/p\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 238\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.53 x 9.21 x 6.14 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eIllustrated:\u003c\/strong\u003e Yes\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e September 29, 2025\u003c\/div\u003e\n            ","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":48032803160285,"sku":"9780367678210","price":130.22,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0811\/9867\/8237\/files\/T3-FwiC-BX9780367678210.webp?v=1781496811","url":"https:\/\/handfulofbooks.com\/products\/machine-learning-in-2d-materials-science-paperback","provider":"Handful of Books","version":"1.0","type":"link"}