Machine learning in 2D materials science:
"Data science and machine learning (ML) methods are increasingly being used to transform the way research is being conducted in materials science to enable new discoveries and design new materials. For any materials science researcher or student, it may be daunting to figure out if ML technique...
Gespeichert in:
| Weitere beteiligte Personen: | , , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Boca Raton, FL
CRC Press
2023
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| Schlagwörter: | |
| Links: | https://learning.oreilly.com/library/view/-/9781000987447/?ar |
| Zusammenfassung: | "Data science and machine learning (ML) methods are increasingly being used to transform the way research is being conducted in materials science to enable new discoveries and design new materials. For any materials science researcher or student, it may be daunting to figure out if ML techniques are useful for them or if so, which ones are applicable in their individual contexts, and how to study the effectiveness of these methods systematically. Machine Learning in 2D Materials Science provides broad coverage of data science and ML fundamentals to 2D materials science researchers so that they can confidently leverage these techniques in their research projects. Offers introductory material in topics such as ML, data integration, and 2D materials. Provides in-depth coverage of current ML methods for validating 2D materials using both experimental and simulation data, researching and discovering new 2D materials, and enhancing ML methods with physical properties of materials. Discusses customized ML methods for 2D materials data and applications and high throughput data acquisition. Describes several case studies illustrating how ML approaches are currently leading innovations in the discovery, development, manufacturing, and deployment of 2D materials needed for strengthening industrial products. Gives future trends in ML for 2D materials, explainable AI, and dealing with extremely large and small diverse datasets. Offers Jupyter Notebooks and datasets for download. Aimed at materials science researchers, this book allows readers to quickly, yet thoroughly learn the ML and AI concepts needed to ascertain the applicability of 2D ML methods in their research"-- |
| Beschreibung: | Includes bibliographical references and index. - Description based on online resource; title from digital title page (viewed on January 31, 2024) |
| Umfang: | 1 Online-Ressource |
| ISBN: | 9781003132981 1003132987 9781000987430 1000987434 1000987442 9781000987447 |
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| 520 | |a "Data science and machine learning (ML) methods are increasingly being used to transform the way research is being conducted in materials science to enable new discoveries and design new materials. For any materials science researcher or student, it may be daunting to figure out if ML techniques are useful for them or if so, which ones are applicable in their individual contexts, and how to study the effectiveness of these methods systematically. Machine Learning in 2D Materials Science provides broad coverage of data science and ML fundamentals to 2D materials science researchers so that they can confidently leverage these techniques in their research projects. Offers introductory material in topics such as ML, data integration, and 2D materials. Provides in-depth coverage of current ML methods for validating 2D materials using both experimental and simulation data, researching and discovering new 2D materials, and enhancing ML methods with physical properties of materials. Discusses customized ML methods for 2D materials data and applications and high throughput data acquisition. Describes several case studies illustrating how ML approaches are currently leading innovations in the discovery, development, manufacturing, and deployment of 2D materials needed for strengthening industrial products. Gives future trends in ML for 2D materials, explainable AI, and dealing with extremely large and small diverse datasets. Offers Jupyter Notebooks and datasets for download. Aimed at materials science researchers, this book allows readers to quickly, yet thoroughly learn the ML and AI concepts needed to ascertain the applicability of 2D ML methods in their research"-- | ||
| 650 | 0 | |a Materials science |x Data processing | |
| 650 | 0 | |a Two-dimensional materials |x Computer simulation | |
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| 650 | 4 | |a Science des matériaux ; Informatique | |
| 650 | 4 | |a TECHNOLOGY / Material Science | |
| 650 | 4 | |a COMPUTERS / Artificial Intelligence | |
| 650 | 4 | |a TECHNOLOGY / Engineering / Chemical & Biochemical | |
| 650 | 4 | |a Machine learning | |
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| id | ZDB-30-ORH-110157109 |
| illustrated | Not Illustrated |
| indexdate | 2025-04-10T09:36:32Z |
| institution | BVB |
| isbn | 9781003132981 1003132987 9781000987430 1000987434 1000987442 9781000987447 |
| language | English |
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| publishDate | 2023 |
| publishDateSearch | 2023 |
| publishDateSort | 2023 |
| publisher | CRC Press |
| record_format | marc |
| spelling | Machine learning in 2D materials science Parvathi Chundi, Venkataramana Gadhamshetty, Bharat K. Jasthi, Carol Lushbough Boca Raton, FL CRC Press 2023 1 Online-Ressource Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Includes bibliographical references and index. - Description based on online resource; title from digital title page (viewed on January 31, 2024) "Data science and machine learning (ML) methods are increasingly being used to transform the way research is being conducted in materials science to enable new discoveries and design new materials. For any materials science researcher or student, it may be daunting to figure out if ML techniques are useful for them or if so, which ones are applicable in their individual contexts, and how to study the effectiveness of these methods systematically. Machine Learning in 2D Materials Science provides broad coverage of data science and ML fundamentals to 2D materials science researchers so that they can confidently leverage these techniques in their research projects. Offers introductory material in topics such as ML, data integration, and 2D materials. Provides in-depth coverage of current ML methods for validating 2D materials using both experimental and simulation data, researching and discovering new 2D materials, and enhancing ML methods with physical properties of materials. Discusses customized ML methods for 2D materials data and applications and high throughput data acquisition. Describes several case studies illustrating how ML approaches are currently leading innovations in the discovery, development, manufacturing, and deployment of 2D materials needed for strengthening industrial products. Gives future trends in ML for 2D materials, explainable AI, and dealing with extremely large and small diverse datasets. Offers Jupyter Notebooks and datasets for download. Aimed at materials science researchers, this book allows readers to quickly, yet thoroughly learn the ML and AI concepts needed to ascertain the applicability of 2D ML methods in their research"-- Materials science Data processing Two-dimensional materials Computer simulation Machine learning Apprentissage automatique Science des matériaux ; Informatique TECHNOLOGY / Material Science COMPUTERS / Artificial Intelligence TECHNOLOGY / Engineering / Chemical & Biochemical Chundi, Parvathi HerausgeberIn edt Gadhamshetty, Venkataramana 1977- HerausgeberIn edt Jasthi, Bharat K. HerausgeberIn edt Lushbough, Carol HerausgeberIn edt 9780367678203 Erscheint auch als Druck-Ausgabe 9780367678203 |
| spellingShingle | Machine learning in 2D materials science Materials science Data processing Two-dimensional materials Computer simulation Machine learning Apprentissage automatique Science des matériaux ; Informatique TECHNOLOGY / Material Science COMPUTERS / Artificial Intelligence TECHNOLOGY / Engineering / Chemical & Biochemical |
| title | Machine learning in 2D materials science |
| title_auth | Machine learning in 2D materials science |
| title_exact_search | Machine learning in 2D materials science |
| title_full | Machine learning in 2D materials science Parvathi Chundi, Venkataramana Gadhamshetty, Bharat K. Jasthi, Carol Lushbough |
| title_fullStr | Machine learning in 2D materials science Parvathi Chundi, Venkataramana Gadhamshetty, Bharat K. Jasthi, Carol Lushbough |
| title_full_unstemmed | Machine learning in 2D materials science Parvathi Chundi, Venkataramana Gadhamshetty, Bharat K. Jasthi, Carol Lushbough |
| title_short | Machine learning in 2D materials science |
| title_sort | machine learning in 2d materials science |
| topic | Materials science Data processing Two-dimensional materials Computer simulation Machine learning Apprentissage automatique Science des matériaux ; Informatique TECHNOLOGY / Material Science COMPUTERS / Artificial Intelligence TECHNOLOGY / Engineering / Chemical & Biochemical |
| topic_facet | Materials science Data processing Two-dimensional materials Computer simulation Machine learning Apprentissage automatique Science des matériaux ; Informatique TECHNOLOGY / Material Science COMPUTERS / Artificial Intelligence TECHNOLOGY / Engineering / Chemical & Biochemical |
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