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Detection of Functional Matrix Metalloproteinases by Zymography

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MXgap: A MXene Learning Tool for Bandgap Prediction.

Diego Ontiveros¹, Sergi Vela², Francesc Viñes¹

¹Departament de Ciència de Materials i Química Física & Institut de Química Teòrica i Computacional (IQTCUB), Universitat de Barcelona, c/Martí i Franquès 1-11, 08028 Barcelona, Spain.

ACS Catalysis

|August 21, 2025

Summary

Machine learning accelerates the discovery of MXenes, novel 2D materials for clean energy. A new Python package, MXgap, efficiently predicts MXene bandgaps for enhanced photocatalysis and water splitting applications.

Keywords:

MXenes density functional theory machine learning photocatalysis water splitting

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Area of Science:

Materials Science
Renewable Energy
Computational Chemistry

Background:

Growing demand for clean energy drives research into advanced photocatalytic materials.
MXenes (2D transition metal carbides/nitrides) show promise for water splitting.
Predicting MXene bandgaps, crucial for photocatalysis, is computationally intensive.

Purpose of the Study:

To develop a machine learning (ML) framework for efficient MXene bandgap prediction.
To accelerate the discovery and optimization of MXenes for photocatalytic applications.
To screen La-based MXenes for water splitting suitability.

Main Methods:

Trained multiple ML models on a dataset of 4356 MXene structures.
Developed a robust classifier-regressor pipeline for bandgap prediction.
Implemented the framework in an open-source Python package (MXgap).

Main Results:

Achieved 92% classification accuracy and 0.17 eV MAE for bandgap prediction.
Screened 396 La-based MXenes, identifying six promising candidates.
Evaluated optical properties and solar-to-hydrogen (STH) efficiency for selected candidates.

Conclusions:

ML significantly accelerates MXene material discovery for energy applications.
The MXgap package provides a valuable tool for high-throughput screening of MXenes.
Identified promising MXene candidates for efficient photocatalytic water splitting.