000			02074nam a22002897a 4500
003			OSt
005			20260714091903.0
008			260714b |||||||| |||| 00| 0 eng d
020			_a978-3031446214
020			_a3031446216
041			_aeng
082			_a006.31 KRI-M
100			_aKrishnan, N. M. Anoop, _988723
245			_aMachine learning for materials discovery : numerical recipes and practical applications
260			_aSwitzerland; _bSpringer Nature Switzerland AG, _c2024.
300			_axx, 279p.
440			_aMachine intelligence for materials science. _vISSN 2948-1813 _988724
505			_aIntroduction -- 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.
520			_aFocusing 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.
650			_aMachine learning. _988725
650			_aArtificial intelligence. _988726
700			_aKodamana, Hariprasad, _988727
700			_aBhattoo, Ravinder, _988728
856			_uhttps://doi.org/10.1007/978-3-031-44622-1
942			_cBK
999			_c200809 _d200809