When Machine Learning Meets 2D Materials: A Review

Bin Lu, Yuze Xia, Yuqian Ren, Miaomiao Xie, Liguo Zhou, Giovanni Vinai, Simon A. Morton, Andrew T.S. Wee, Wilfred G. van der Wiel, Wen Zhang*, Ping Kwan Johnny Wong*

*Corresponding author for this work

Research output: Contribution to journalReview articleAcademicpeer-review

1 Citation (Scopus)
27 Downloads (Pure)


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.

Original languageEnglish
Article number2305277
Number of pages40
JournalAdvanced science
Issue number13
Early online date26 Jan 2024
Publication statusPublished - 3 Apr 2024


  • 2D materials
  • data-driven approach
  • machine learning


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