Journal article
Prediction of Synthesis of 2D Metal Carbides and Nitrides (MXenes) and Their Precursors with Positive and Unlabeled Machine Learning
ACS nano, v 13(3), pp 3031-3041
26 Mar 2019
PMID: 30830760
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Growing interest in the potential applications of two-dimensional (2D) materials has fueled advancement in the identification of 2D systems with exotic properties. Increasingly, the bottleneck in this field is the synthesis of these materials. Although theoretical calculations have predicted a myriad of promising 2D materials, only a few dozen have been experimentally realized since the initial discovery of graphene. Here, we adapt the state-of-the-art positive and unlabeled (PU) machine learning framework to predict which theoretically proposed 2D materials have the highest likelihood of being successfully synthesized. Using elemental information and data from high-throughput density functional theory calculations, we apply the PU learning method to the MXene family of 2D transition metal carbides, carbonitrides, and nitrides, and their layered precursor MAX phases, and identify 18 MXene compounds that are highly promising candidates for synthesis. By considering both the MXenes and their precursors, we further propose 20 synthesizable MAX phases that can be chemically exfoliated to produce MXenes.
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Details
- Title
- Prediction of Synthesis of 2D Metal Carbides and Nitrides (MXenes) and Their Precursors with Positive and Unlabeled Machine Learning
- Creators
- Nathan C Frey - Department of Materials Science and EngineeringJin Wang - Department of Materials Science and EngineeringGabriel Iván Vega Bellido - University of Puerto Rico at MayagüezBabak Anasori - Department of Materials Science and Engineering and A.J. Drexel Nanomaterials InstituteYury Gogotsi - Department of Materials Science and Engineering and A.J. Drexel Nanomaterials InstituteVivek B Shenoy - Department of Materials Science and Engineering
- Publication Details
- ACS nano, v 13(3), pp 3031-3041
- Publisher
- American Chemical Society; Washington, DC
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Materials Science and Engineering
- Web of Science ID
- WOS:000462950500031
- Scopus ID
- 2-s2.0-85063448534
- Other Identifier
- 991014970148504721
UN Sustainable Development Goals (SDGs)
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InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Chemistry, Multidisciplinary
- Chemistry, Physical
- Materials Science, Multidisciplinary
- Nanoscience & Nanotechnology