Data-Based Methods for Materials Design and Discovery - Pilania, Ghanshyam

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Résumé :
Machine learning methods are changing the way we design and discover new materials. This book provides an overview of approaches successfully used in addressing materials problems (alloys, ferroelectrics, dielectrics) with a focus on probabilistic methods, such as Gaussian processes, to accurately estimate density functions. The authors, who have extensive experience in this interdisciplinary field, discuss generalizations where more than one competing material property is involved or data with differing degrees of precision/costs or fidelity/expense needs to be considered.

Biographie:
Ghanshyam Pilania is a scientist in the Materials Science and Technology Division at Los Alamos National Laboratory (LANL). He received a B.Tech. in Metallurgical and Materials Engineering from Indian Institute of Technology Roorkee, India in 2007, followed by a Ph.D. in Materials Science and Engineering from University of Connecticut, Storrs, in 2012. His four year postdoctoral work was supported by a LANL Directors' postdoctoral fellowship award and an Alexander von Humboldt postdoctoral fellowship at the Fritz Haber Institute of the Max Planck Society. His current research interests broadly include developing and applying high throughput electronic structure and atomistic methods to understand and design functional materials, with a particular focus on targeted materials design and discovery using materials informatics and machine learning based techniques.Prasanna V. Balachandran is currently an Assistant Professor with a joint appointment in the Department of Materials Science andEngineering and Department of Mechanical and Aerospace Engineering in University of Virginia (UVA). He earned his Bachelors' Degree in Metallurgical Engineering from Anna University, India in 2007 and a Ph.D. in Materials Science and Engineering from Iowa State University, in 2011. Prior to joining UVA in December 2017, he spent three and half years as a postdoctoral research associate in the Theoretical Division at Los Alamos National Laboratory (LANL), and two years as a postdoctoral research associate at Drexel University. His research interests are interdisciplinary spanning diverse areas such as crystal symmetry, first-principles-based density functional theory calculations, and information science methods for accelerating the design and discovery of new materials.