Substrate-aware computational design of two-dimensional materials

Two-dimensional (2D) materials attract considerable attention due to their remarkable electronic, mechanical and optical properties. Despite their use in combination with substrates in practical applications, computational studies often neglect the effects of substrate interactions for simplicity. This study presents a novel method for predicting the atomic structure of 2D materials on substrates by combining an evolutionary algorithm, a lattice-matching technique, an automated machine-learning interatomic potentials training protocol, and the ab initio thermodynamics approach. Using the molybdenum-sulfur system on a sapphire substrate as a case study, we reveal several new stable and metastable structures, including previously known 1H-MoS₂ and newly found Pmma Mo₃S₂, \(P\bar{1}\) Mo₂S, P2₁m Mo₅S₃, and P4mm Mo₄S, where the Mo₄S structure is specifically stabilized by interaction with the substrate. Finally, we use the ab initio thermodynamics approach to predict the synthesis conditions of the discovered structures in the parameter space of the commonly used chemical vapor deposition technique.