000 03624nam a22006135i 4500
001 978-3-031-44622-1
003 DE-He213
005 20251030112029.0
007 cr nn 008mamaa
008 240506s2024 sz | s |||| 0|eng d
020 _a9783031446221
_9978-3-031-44622-1
024 7 _a10.1007/978-3-031-44622-1
_2doi
050 4 _aTA404.23
072 7 _aTGM
_2bicssc
072 7 _aTEC021000
_2bisacsh
072 7 _aTGM
_2thema
100 1 _aKrishnan, N. M. Anoop.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_976431
245 1 0 _aMachine Learning for Materials Discovery
_h[electronic resource] :
_bNumerical Recipes and Practical Applications /
_cby N. M. Anoop Krishnan, Hariprasad Kodamana, Ravinder Bhattoo.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2024.
300 _aXX, 279 p. 110 illus., 95 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aMachine Intelligence for Materials Science,
_x2948-1821
505 0 _aPart I: Introduction -- Part II: Basics of Machine Learning Methods -- Introduction to Data-Based Modeling -- Model Development -- Introduction to Machine Learning -- Quick Dive into Probabilistic Methods -- Optimization -- Part III: Application in Glass Science -- Property Prediction -- Glass Discovery -- Understanding Glass Physics -- Atomistic Modeling -- Future Directions.
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 0 _aMaterials science
_xData processing.
_967985
650 0 _aMachine learning.
_955059
650 0 _aSystem theory.
_966519
650 0 _aMathematical physics.
650 0 _aCeramic materials.
_967115
650 1 4 _aComputational Materials Science.
_967987
650 2 4 _aMachine Learning.
_955059
650 2 4 _aComplex Systems.
_966520
650 2 4 _aTheoretical, Mathematical and Computational Physics.
_965563
650 2 4 _aCeramics.
_967117
700 1 _aKodamana, Hariprasad.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_976432
700 1 _aBhattoo, Ravinder.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_976433
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031446214
776 0 8 _iPrinted edition:
_z9783031446238
776 0 8 _iPrinted edition:
_z9783031446245
830 0 _aMachine Intelligence for Materials Science,
_x2948-1821
_968417
856 4 0 _uhttps://doi.org/10.1007/978-3-031-44622-1
912 _aZDB-2-PHA
912 _aZDB-2-SXP
999 _c59670
_d59670