When Machine Learning Meets 2D Materials : A Review
© 2024 The Authors. Advanced Science published by Wiley‐VCH GmbH..
The availability of an ever-expanding portfolio of 2D materials with rich internal degrees of freedom (spin, excitonic, valley, sublattice, and layer pseudospin) together with the unique ability to tailor heterostructures made layer by layer in a precisely chosen stacking sequence and relative crystallographic alignments, offers an unprecedented platform for realizing materials by design. However, the breadth of multi-dimensional parameter space and massive data sets involved is emblematic of complex, resource-intensive experimentation, which not only challenges the current state of the art but also renders exhaustive sampling untenable. To this end, machine learning, a very powerful data-driven approach and subset of artificial intelligence, is a potential game-changer, enabling a cheaper - yet more efficient - alternative to traditional computational strategies. It is also a new paradigm for autonomous experimentation for accelerated discovery and machine-assisted design of functional 2D materials and heterostructures. Here, the study reviews the recent progress and challenges of such endeavors, and highlight various emerging opportunities in this frontier research area.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:11 |
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| Enthalten in: |
Advanced science (Weinheim, Baden-Wurttemberg, Germany) - 11(2024), 13 vom: 12. Apr., Seite e2305277 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Lu, Bin [VerfasserIn] |
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| Links: |
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| Themen: |
2D materials |
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| Anmerkungen: |
Date Revised 04.04.2024 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1002/advs.202305277 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a The availability of an ever-expanding portfolio of 2D materials with rich internal degrees of freedom (spin, excitonic, valley, sublattice, and layer pseudospin) together with the unique ability to tailor heterostructures made layer by layer in a precisely chosen stacking sequence and relative crystallographic alignments, offers an unprecedented platform for realizing materials by design. However, the breadth of multi-dimensional parameter space and massive data sets involved is emblematic of complex, resource-intensive experimentation, which not only challenges the current state of the art but also renders exhaustive sampling untenable. To this end, machine learning, a very powerful data-driven approach and subset of artificial intelligence, is a potential game-changer, enabling a cheaper - yet more efficient - alternative to traditional computational strategies. It is also a new paradigm for autonomous experimentation for accelerated discovery and machine-assisted design of functional 2D materials and heterostructures. Here, the study reviews the recent progress and challenges of such endeavors, and highlight various emerging opportunities in this frontier research area | ||
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| 700 | 1 | |a Ren, Yuqian |e verfasserin |4 aut | |
| 700 | 1 | |a Xie, Miaomiao |e verfasserin |4 aut | |
| 700 | 1 | |a Zhou, Liguo |e verfasserin |4 aut | |
| 700 | 1 | |a Vinai, Giovanni |e verfasserin |4 aut | |
| 700 | 1 | |a Morton, Simon A |e verfasserin |4 aut | |
| 700 | 1 | |a Wee, Andrew T S |e verfasserin |4 aut | |
| 700 | 1 | |a van der Wiel, Wilfred G |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Wen |e verfasserin |4 aut | |
| 700 | 1 | |a Wong, Ping Kwan Johnny |e verfasserin |4 aut | |
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