Machine learning for materials discovery : numerical recipes and practical applications
Material type:
TextLanguage: English Series: Machine intelligence for materials science ; ISSN 2948-1813Publication details: Switzerland; Springer Nature Switzerland AG, 2024.Description: xx, 279pISBN: - 978-3031446214
- 3031446216
- 006.31 KRI-M
| Item type | Current library | Home library | Collection | Call number | Materials specified | Status | Date due | Barcode | |
|---|---|---|---|---|---|---|---|---|---|
| Books and Monographs | Central Library, NIT Jalandhar General Stacks | Central Library, NIT Jalandhar | Chemical Engineering | 006.31 KRI-M (Browse shelf(Opens below)) | Available | 103072 |
Introduction --
Basics of machine learning --
Data visualization and preprocessing --
Regression methods --
Dimensionality reduction --
Deep learning --
Interpretable machine learning --
Machine learning for materials modelling --
Property prediction --
Materials discovery --
Machine-learned simulations --
Image-based prediction --
Natural language processing.
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.