Machine learning and DFT-assisted design of A2B2X6 2D materials for photocatalytic CO2 reduction

Materials Today Communications, Journal Year: 2025, Volume and Issue: unknown, P. 112016 - 112016

Published: Feb. 1, 2025

Language: Английский

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Recent Progress in the Design and Application of Machine Learning for the Hydrogen Evolution Reaction in Electrocatalysis and Photocatalysis

Materials Today Communications, Journal Year: 2025, Volume and Issue: unknown, P. 112462 - 112462

Published: April 1, 2025

Language: Английский

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Full Prediction of Band Potentials in Semiconductor Materials

Published: Jan. 1, 2024

A machine learning (ML) framework to predict the physical band potentials for a range of semiconductor materials, from metal sulfide, oxide, and nitride, oxysulfide oxynitride, is hereby described. valence maximum (VBM) model was established via transfer relatively large dataset 2D materials (1382 samples, but with incorrect VBM potentials) much smaller physically measured bulk 3D (87 samples) on crystal graph convolutional neural network. This gave predictions experimental accuracy wide (RMSE = 0.27 eV), which 3-fold improvement compared ML trained without 0.75 eV). When combined bandgap prediction 0.29 full conduction can be made, best our knowledge, first any framework. The variation across low-index surfaces predicted correctly verified reported potentials. In fact, able capture in associated minor atomic position alterations, thus circumventing need computationally expensive structural relaxation steps. Based this, general trend between surface displacement shift observed various materials. not yet cope major rearrangement sequence layers, i.e., reconstructions., since it such data. Further current using specifically designed required make reliable such.

Language: Английский

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Photo-Antibacterial Activity of Two-Dimensional (2D)-Based Hybrid Materials: Effective Treatment Strategy for Controlling Bacterial Infection

Antibiotics, Journal Year: 2023, Volume and Issue: 12(2), P. 398 - 398

Published: Feb. 16, 2023

Bacterial contamination in water bodies is a severe scourge that affects human health and causes mortality morbidity. Researchers continue to develop next-generation materials for controlling bacterial infections from water. Photo-antibacterial activity continues gain the interest of researchers due its adequate, rapid, antibiotic-free process. do not have any side effects minimal chance developing resistance their rapid efficacy. Photocatalytic two-dimensional nanomaterials (2D-NMs) great potential control infection exceptional properties, such as high surface area, tunable band gap, specific structure, functional groups. Moreover, optical electric properties 2D-NMs might be tuned by creating heterojunctions or doping metals/carbon/polymers, subsequently enhancing photo-antibacterial ability. This review article focuses on synthesis 2D-NM-based hybrid materials, effect dopants 2D-NMs, application. We also discuss how we could improve photo-antibacterials using different strategies role artificial intelligence (AI) photocatalyst degradation pollutants. Finally, was improving toxicity mechanism, challenges.

Language: Английский

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Full prediction of band potentials in semiconductor materials

Materials Today Physics, Journal Year: 2024, Volume and Issue: 46, P. 101519 - 101519

Published: July 23, 2024

Language: Английский

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Construction of hybrid models based on cascade technique using basic machine learning models: An application as photocurrent density predictor of the photoelectrode in PEC cell

Materials Today Communications, Journal Year: 2024, Volume and Issue: 41, P. 110643 - 110643

Published: Oct. 10, 2024

Language: Английский

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https://2DMat.ChemDX.org: Experimental data platform for 2D materials from synthesis to physical properties

Digital Discovery, Journal Year: 2024, Volume and Issue: 3(3), P. 573 - 585

Published: Jan. 1, 2024

https://2DMat.ChemDX.org is a comprehensive data platform tailored for 2D materials research, emphasizing the handling and analysis of experimental through specialized management, visualization, machine learning tools.

Language: Английский

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Artificial Intelligence for Developing Smart and Sustainable Energy Systems

Elsevier eBooks, Journal Year: 2024, Volume and Issue: unknown, P. 403 - 424

Published: Jan. 1, 2024

Language: Английский

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A family of flexible two-dimensional semiconductors: MgMX₂Y₆ (M = Ti/Zr/Hf; X = Si/Ge; Y = S/Se/Te)

Journal of Semiconductors, Journal Year: 2023, Volume and Issue: 44(4), P. 042101 - 042101

Published: April 1, 2023

Abstract Inspired by the recently predicted 2D MX 2 Y 6 (M = metal element; X Si/Ge/Sn; S/Se/Te), we explore possible applications of alkaline earth (using magnesium as example) in this family based on idea element replacement and valence electron balance. Herein, report a new quaternary compounds, namely MgMX Ti/Zr/Hf; Si/Ge; S/Se/Te) monolayers, with superior kinetic, thermodynamic mechanical stability. In addition, our results indicate that monolayers are all indirect band gap semiconductors values ranging from 0.870 to 2.500 eV. Moreover, edges optical properties suitable for constructing multifunctional optoelectronic devices. Furthermore, comparison, mechanical, electronic have been discussed detail. The success introducing Mg into indicates more potential materials, such Ca- Sr-based may be discovered future. Therefore, work not only broadens existing semiconductors, but it also provides beneficial

Language: Английский

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Machine learning of the Γ-point gap and flat bands of twisted bilayer graphene at arbitrary angles

Chinese Physics B, Journal Year: 2023, Volume and Issue: 32(5), P. 057306 - 057306

Published: Jan. 13, 2023

The novel electronic properties of bilayer graphene can be fine-tuned via twisting, which may induce flat bands around the Fermi level with nontrivial topology. In general, band structure such twisted (TBG) theoretically obtained by using first-principles calculations, tight-binding method, or continuum model, are either computationally demanding parameters dependent. this work, sure independence screening sparsifying operator we propose a physically interpretable three-dimensional (3D) descriptor utilized to readily obtain Γ -point gap TBG at arbitrary twist angles and different interlayer spacings. strong predictive power is demonstrated high Pearson coefficient 99% for both training testing data. To go further, adopt neural network algorithm accurately probe various angles, accelerate study correlation physics associated fundamental characteristic, especially those systems larger number atoms in unit cell.

Language: Английский

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