High-Throughput Exploration of Ti–V–Nb–Mo Carbide MXenes Using Neural Network Potentials and Their Evaluation as Catalysts for Hydrogen Evolution Reaction

Realization of a sustainable hydrogen economy in the
future requires
the development of efficient and cost-effective catalysts for its
production at scale. MXenes (Mn+1Xn) are a class
of 2D materials with ‘n’ layers of carbon or nitrogen
(X) interleaved by ‘n+1’ layers of transition metal
(M) and have emerged as promising materials for various applications
including catalysts for hydrogen evolution reaction (HER). Their properties
are intimately related to both their composition and their atomic
structure. Recently, high entropy MXenes were synthesized, opening
a vast compositional space of potentially stable and functionally
superior materials. Detailed atomistic modeling enables us to systematically
explore this extensive design space, which is otherwise infeasible
in experiments. We have developed a Neural Network Potential (NNP)
to model (TixVyNbzMop)n+1Cn MXenes (x+y+z+p = 1; n = 1,2,3) by training against Density
Functional Theory (DFT) data in an active learning fashion. We then
used the developed NNP to perform hybrid Monte Carlo-Molecular Dynamics
(MC-MD) simulations to identify thermodynamically stable compositions
and investigate the relative arrangement of transition metal atoms
within and across layers. Thermodynamic stability increased with Mo
content and its presence on the surface layer. We further investigated
the catalytic performance of stable MXenes for the HER and observed
that the center of the oxygen p-band (εp) correlated
well with the energy of adsorption of a hydrogen atom ΔG(*H).
Subsurface metal atoms significantly influenced the ΔG(*H) values
at the surface via both ligand and strain effects. Our work expands
the space of potentially stable MXene compositions, providing targets
for synthesis and their evaluation in various applications.