First-principles statistical investigation of thermodynamic behavior with excitonic effects in Mo <sub> 1− <i>x</i> </sub> W <sub> <i>x</i> </sub> Se <sub>2</sub> alloys through a data-driven workflow approach

We investigate the thermodynamic stability of the Mo 1− x W x Se 2 alloy at the atomistic level and introduce an innovative cost-effective protocol for predicting and characterizing its optoelectronic properties, explicitly incorporating excitonic effects.

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Publication Details

Journal
Journal of Materials Chemistry A
Published
2025-01-01
DOI
https://doi.org/10.1039/d5ta02721g
Primary Topic
Machine Learning in Materials Science
Type
article
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article

First-principles statistical investigation of thermodynamic behavior with excitonic effects in Mo 1− x W x Se 2 alloys through a data-driven workflow approach

Maurício J. Piotrowski, Celso R. C. Rêgo, Ingrid D. Barcelos, Diego Guedes‐Sobrinho et al.
Journal of Materials Chemistry A
Machine Learning in Materials Science
article

First-principles statistical investigation of thermodynamic behavior with excitonic effects in Mo 1− x W x Se 2 alloys through a data-driven workflow approach

Maurício J. Piotrowski, Celso R. C. Rêgo, Ingrid D. Barcelos, Diego Guedes‐Sobrinho, Danilo Neves Silveira, Wolfgang Wenzel, Alexandre C. Dias, Ana Carolina Ferreira de Brito, Andréia Luísa da Rosa, Carlos M. O. Bastos
article en

Abstract

We investigate the thermodynamic stability of the Mo 1− x W x Se 2 alloy at the atomistic level and introduce an innovative cost-effective protocol for predicting and characterizing its optoelectronic properties, explicitly incorporating excitonic effects.

Journal of Materials Chemistry AVol. 13(45)
Karlsruhe Institute of Technology (DE), Universidade de Brasília (BR), Universidade Federal de Pelotas (BR), Institute of Physics (AZ), Brazilian Center for Research in Energy and Materials (BR), Universidade Federal do Paraná (BR), Universidade Federal de Goiás (BR)
Openalex Percentile: Top 78%
Machine Learning in Materials Science
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