Machine learning-enabled prediction of the electronic band-edge shapes and properties of 2D transition metal dichalcogenide alloys

Materials Advances 2026 Open access

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Authors: Tarvir Anjum Aditto, Vivek Chowdhury, Hafiz Imtiaz, Ahmed Zubair
DOI: 10.1039/d5ma01485a
Cited by: 5
References: 31

Abstract

A data-driven extra trees model predicts conduction and valence band structures of 2D TMD alloys directly from composition, achieving near-DFT accuracy while dramatically reducing computational cost.

Fields of Study & Subjects

2D Materials and Applications Machine Learning in Materials Science Inorganic Chemistry and Materials

Publication details

Journal / source
Materials Advances
Publisher
Royal Society of Chemistry (RSC)
Volume
7
Issue
7
Pages
3767–3780
Publication date
1 January 2026
Language
EN