Machine learning assisted High-Throughput study of M$_4$X$_3$T$_x$ MXenes

Sakshi Goel; Arti Kashyap

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arXiv · 2603.04103

Machine learning assisted High-Throughput study of M$_4$X$_3$T$_x$ MXenes

Sakshi Goel·Arti Kashyap

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Abstract

In this work, we employ a machine-learning-assisted high-throughput density functional theory framework to systematically investigate the stability, electronic structure, and magnetic ground states of 234 M$_4$X$_3$T$_x$ MXenes. The machine learning model predicts lattice parameters with up to 94% accuracy using a relatively small training dataset and significantly reduces structural optimization time in high-throughput calculations. Based on total energy and density-of-states analyses, we classify the magnetic nature of MXenes across different transition- metal compositions and surface terminations. Ti-, Zr-, Hf-, Nb-, and Ta-based MXenes are found to be non-magnetic metals for all functional groups considered, while Sc- and Y-based systems exhibit a range of behaviors including weak ferromagnetism and semiconducting character. V- and Fe-based MXenes are identified as antiferromagnetic metals, whereas Cr- and Mn-based MXenes yield 16 ferromagnetic systems with spin polarization ranging from 50% to 100%.

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Sakshi Goel, Arti Kashyap. 2026-03-04. Machine learning assisted High-Throughput study of M$_4$X$_3$T$_x$ MXenes. https://arxiv.org/abs/2603.04103

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