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024 7 _a10.1007/978-981-97-0217-6
_2doi
050 4 _aTA404.23
072 7 _aTGM
_2bicssc
072 7 _aTEC021000
_2bisacsh
072 7 _aTGM
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100 1 _aTakahashi, Keisuke.
_eauthor.
_0(orcid)0000-0002-9328-1694
_1https://orcid.org/0000-0002-9328-1694
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_974458
245 1 0 _aMaterials Informatics and Catalysts Informatics
_h[electronic resource] :
_bAn Introduction /
_cby Keisuke Takahashi, Lauren Takahashi.
250 _a1st ed. 2024.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2024.
300 _aIX, 297 p. 1 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aChapter 1. An Introduction to Materials Informatics and Catalysts Informatics -- Chapter 2. Developing an Informatics Work Environment -- Chapter 3. Programming -- Chapter 4. Programming and Python -- Chapter 5. Data and Materials and Catalysts Informatics -- Chapter 6. Data Visualization -- Chapter 7. Machine Learning -- Chapter 8. Supervised Machine Learning -- Chapter 9. Unsupervised Machine Learning and Beyond Machine Learning.
520 _aThis textbook is designed for students and researchers who are interested in materials and catalysts informatics with little to no prior experience in data science or programming languages. Starting with a comprehensive overview of the concept and historical context of materials and catalysts informatics, it serves as a guide for establishing a robust materials informatics environment. This essential resource is designed to teach vital skills and techniques required for conducting informatics-driven research, including the intersection of hardware, software, programming, machine learning within the field of data science and informatics. Readers will explore fundamental programming techniques, with a specific focus on Python, a versatile and widely-used language in the field. The textbook explores various machine learning techniques, equipping learners with the knowledge to harness the power of data science effectively. The textbook provides Python code examples, demonstrating materials informatics applications, and offers a deeper understanding through real-world case studies using materials and catalysts data. This practical exposure ensures readers are fully prepared to embark on their informatics-driven research endeavors upon completing the textbook. Instructors will also find immense value in this resource, as it consolidates the skills and information required for materials informatics into one comprehensive repository. This streamlines the course development process, significantly reducing the time spent on creating course material. Instructors can leverage this solid foundation to craft engaging and informative lecture content, making the teaching process more efficient and effective. .
650 0 _aMaterials science
_xData processing.
_967985
650 0 _aChemoinformatics.
_970652
650 0 _aCatalysis.
_966843
650 0 _aChemistry
_xData processing.
_969913
650 0 _aGraph theory.
650 1 4 _aComputational Materials Science.
_967987
650 2 4 _aCheminformatics.
_965190
650 2 4 _aCatalysis.
_966843
650 2 4 _aComputational Chemistry.
_969915
650 2 4 _aGraph Theory.
700 1 _aTakahashi, Lauren.
_eauthor.
_0(orcid)0000-0001-9922-8889
_1https://orcid.org/0000-0001-9922-8889
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_974459
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789819702169
776 0 8 _iPrinted edition:
_z9789819702183
776 0 8 _iPrinted edition:
_z9789819702190
856 4 0 _uhttps://doi.org/10.1007/978-981-97-0217-6
912 _aZDB-2-CMS
912 _aZDB-2-SXC
999 _c58834
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