Multi-Fidelity Computational Screening of High-Entropy MBenes for CO$_2$ Electroreduction

TL;DR

This study introduces a comprehensive computational framework combining DFT, machine learning, and AIMD to identify and evaluate high-entropy 2D materials for CO2 electroreduction.

Contribution

It develops an integrated DFT-MLIP-AIMD approach for screening and designing high-entropy MBenes as efficient electrocatalysts for CO2 conversion.

Findings

55 out of 56 candidates are structurally stable.

The machine learning interatomic potential achieved low energy RMSEs.

Active sites identified match metal d-orbitals with CO2 orbitals.

Abstract

High-entropy MBenes (HE-MBenes) represent a promising, unexplored class of 2D materials for electrocatalysis. In this work, we present a systematic computational screening of 56 equiatomic quinary HE-MBene compositions from the {Ti, V, Cr, Mo, Nb, Ta, Zr, Hf} pool for CO$_2$ adsorption and electroreduction. Using the Monte Carlo Special Quasirandom Structure (MCSQS) algorithm, we generated disordered M$_1B_1$-type supercells and assessed structural stability via DFT (PBE+D3) in VASP. Of the 56 candidates, 55 passed relaxation, with 45 exhibiting negative formation energies, confirming thermodynamic stability. To efficiently screen CO$_2$ adsorption across disordered surfaces, we developed a machine-learning interatomic potential (MLIP) using the MACE architecture. Fine-tuned on our DFT dataset, the model achieved energy RMSEs of 3.49 and 3.0 meV/atom for adsorbed and pristine sets, respectively. Active sites were identified via PDOS analysis, matching metal d-orbital signatures with CO$_2$ molecular orbitals. The rate-determining step of the CO$_2$-to-CO pathway was evaluated using the computational hydrogen electrode (CHE) model. Short-time structural integrity was assessed via AIMD at 500 K over 2.5 ps; phonon-based stability remains a priority for future work. Our results establish an integrated DFT-MLIP-AIMD framework for the rational design of high-entropy 2D materials tailored for CO$_2$ conversion.