Large-Scale Integration of Experimental and Computational Data for 2D Materials

The past decade has seen rapid growth in the number of experimentally realized two-dimensional (2D) materials with diverse chemical and physical properties. However, information on their crystal structure, synthesis routes, and measured or predicted properties remains scattered across thousands of publications. Here, we consolidate this fragmented knowledge by establishing X2DB─an open infrastructure that integrates experimental and computational data on 2D materials. Using extensive literature mining and direct community uploads, we identify 370 unique 2D materials that have been realized in monolayer or few-layer form and link them to their digital counterparts in computational databases, enabling consistent ab initio characterization of their properties across monolayer, bilayer, and bulk forms. We describe the structure and content of the database, highlight its support for community uploads, illustrate how it can be used to generate scientific insight, and introduce a hierarchical classification of the known set of 2D materials. Our work supports the integration and cross-fertilization of experimental and theoretical knowledge and contributes to data-driven and predictive synthesis of 2D materials.

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Publication Details

Journal
ACS Nano
Published
2026-04-07
DOI
https://doi.org/10.1021/acsnano.6c01514
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.00

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article

Large-Scale Integration of Experimental and Computational Data for 2D Materials

Søren Ulstrup, Thomas Olsen, András Kis, Søren Raza et al.
ACS Nano
Machine Learning in Materials Science
article

Large-Scale Integration of Experimental and Computational Data for 2D Materials

Søren Ulstrup, Thomas Olsen, András Kis, Søren Raza, Meng Zhao, Philip Hofmann, Mikkel Ohm Sauer, J. L. Mortensen, Matthias Batzill, Stela Canulescu, Tara M. Boland, Jinghua Teng, Wencai Ren, Xiaoxu Zhao, Yury Gogotsi, Jiong Lu, Thomas Michely, Kristian S. Thygesen, M. Amin Akhound, B. Moses Abraham, Zdenek Sofer
article en

Abstract

The past decade has seen rapid growth in the number of experimentally realized two-dimensional (2D) materials with diverse chemical and physical properties. However, information on their crystal structure, synthesis routes, and measured or predicted properties remains scattered across thousands of publications. Here, we consolidate this fragmented knowledge by establishing X2DB─an open infrastructure that integrates experimental and computational data on 2D materials. Using extensive literature mining and direct community uploads, we identify 370 unique 2D materials that have been realized in monolayer or few-layer form and link them to their digital counterparts in computational databases, enabling consistent ab initio characterization of their properties across monolayer, bilayer, and bulk forms. We describe the structure and content of the database, highlight its support for community uploads, illustrate how it can be used to generate scientific insight, and introduce a hierarchical classification of the known set of 2D materials. Our work supports the integration and cross-fertilization of experimental and theoretical knowledge and contributes to data-driven and predictive synthesis of 2D materials.

ACS NanoVol. 20(15)
Agency for Science, Technology and Research (SG), Pennsylvania State University (US), National University of Singapore (SG), University of Cologne (DE), Chinese Academy of Sciences (CN), Peking University (CN), Aarhus University (DK), University of South Florida (US), Institute of Molecular Functional Materials (CN), Institute of Materials Research and Engineering (SG), Campo Arqueologico de Mertola (PT), École Polytechnique Fédérale de Lausanne (CH), Drexel University (US), University of Chemistry and Technology, Prague (CZ), Technical University of Denmark (DK)
Villum Fonden
Openalex Percentile: Top 30%
Machine Learning in Materials Science
DOIOpen Access PDFOpenAlex
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