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

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%.

• Publication year

  2026

• Venue

  Unknown venue

• Publication date

  2026-03-04

• Fields of study

  Materials Science, Physics

• Identifiers
  arXiv 2603.04103
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• ROS-driven antibacterial mechanisms of multi-metallic (TiVNbMo)₄C₃Tx MXene

  2026cited by this paper
• Machine learning-enhanced exploration of 2D MXene van der Waals heterostructures for energy storage

  2026cited by this paper
• First-principles and machine-learning approaches for interpreting and predicting the properties of MXenes

  2025cited by this paper
• Density functional theory and material databases in the era of machine learning

  2024cited by this paper
• M4X3 MXenes: Application in Energy Storage Devices

  2024cited by this paper
• Magnetic MXene: A Machine learning model with small data

  2023cited by this paper
• Implementation strategies in phonopy and phono3py

  2023cited by this paper
• Potential of MXenes as a novel material for spintronic devices: a review.

  2023cited by this paper
• Theoretical Investigations of Ti4c3 and Ti4c3t2 (T= F, O and Oh) Monolayers as Anode Materials for Li-Ion Batteries

  2023cited by this paper
• Machine learning accelerated study for predicting the lattice constant and substitution energy of metal doped titanium dioxide

  2023cited by this paper
• Machine Learning-Accelerated Discovery of Novel 2D Ferromagnetic Materials with Strong Magnetization

  2023cited by this paper
• Machine Learning-Aided High-Throughput First-Principles Calculations to Predict the Formation Energy of μ Phase

  2023cited by this paper
• High-throughput materials screening algorithm based on first-principles density functional theory and artificial neural network for high-entropy alloys

  2022cited by this paper
• MXene chemistry, electrochemistry and energy storage applications

  2022cited by this paper
• Predicting the Crystal Structure and Lattice Parameters of the Perovskite Materials via Different Machine Learning Models Based on Basic Atom Properties

  2022cited by this paper
• Vanadium Carbide (V4C3) MXene as an Efficient Anode for Li-Ion and Na-Ion Batteries

  2022cited by this paper
• Ten Years of Progress in the Synthesis and Development of MXenes

  2021cited by this paper
• First-principles investigation of the structural and dynamical stability, electronic and thermal properties of two-dimensional Yn+1Cn (n =1, 2, and 3) MXenes

  2021cited by this paper
• Recent progress of the Computational 2D Materials Database (C2DB)

  2021cited by this paper
• Machine learning lattice constants for spinel compounds

  2020cited by this paper
• MXetronics: MXene-Enabled Electronic and Photonic Devices

  2020cited by this paper
• Machine learning based prediction of lattice thermal conductivity for half-Heusler compounds using atomic information

  2020cited by this paper
• Mlatticeabc: Generic Lattice Constant Prediction of Crystal Materials Using Machine Learning

  2020cited by this paper
• Study on the magnetic properties of differently functionalized multilayered Ti3C2Tx MXenes and Ti-Al-C carbides

  2019cited by this paper
• Cr2TiC2-based double MXenes: novel 2D bipolar antiferromagnetic semiconductor with gate-controllable spin orientation toward antiferromagnetic spintronics.

  2018cited by this paper
• High magnetoresistance in ultra-thin two-dimensional Cr-based MXenes.

  2018cited by this paper
• Superior structural, elastic and electronic properties of 2D titanium nitride MXenes over carbide MXenes: a comprehensive first principles study

  2018cited by this paper
• The atomic simulation environment-a Python library for working with atoms.

  2017cited by this paper
• Tunable Magnetism and Transport Properties in Nitride MXenes.

  2017cited by this paper
• Emergence of ferrimagnetic half-metallicity in two-dimensional MXene Mo3N2F2

  2017cited by this paper
• Theoretical prediction of robust and intrinsic half-metallicity in Ni 2 N MXene with different types of surface terminations

  2017cited by this paper
• A Theoretical Prediction on the Intrinsic Half-Metallicity in the Surface-Oxygen-Passivated Cr2N MXene

  2016cited by this paper
• Synthesis of two-dimensional titanium nitride Ti4N3 (MXene).

  2016cited by this paper
• Synthesis and characterization of two-dimensional Nb4C3 (MXene).

  2014cited by this paper
• Hybrid Density Functional Study of Structural and Electronic Properties of Functionalized \ce{Ti_{n+1}X_n} (X= C, N) monolayers

  2013cited by this paper
• Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis

  2012cited by this paper
• Calibration of the DFT/GGA+U Method for Determination of Reduction Energies for Transition and Rare Earth Metal Oxides of Ti, V, Mo, and Ce.

  2011cited by this paper
• Scikit-learn: Machine Learning in Python

  2011cited by this paper
• From ultrasoft pseudopotentials to the projector augmented-wave method

  1999cited by this paper
• Electron-energy-loss spectra and the structural stability of nickel oxide: An LSDA+U study

  1998cited by this paper
• Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set.

  1996cited by this paper
• Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set

  1996cited by this paper
• Generalized Gradient Approximation Made Simple.

  1996cited by this paper

• No citing papers are available for this paper.