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Meeting

2027 TMS Annual Meeting & Exhibition

Symposium

Computational Discovery and Design of Materials

Presentation Title

Size Effects on Chemical Ordering in High-Entropy MXenes: A First-Principles Validated Atomistic Study

Author(s)

Shimanta Das, Noah Oyeniran, Oyshee Chowdhury, Panchapakesan Ganesh, Jingsong J Huang, Chongze Hu

On-Site Speaker (Planned)

Shimanta Das

Abstract Scope

Accurately predicting chemical ordering, including out-of-plane symmetry order and in-plane short range order (SRO), in two-dimensional (2D) high-entropy (HE) MXenes remains challenging because local atomic arrangements are difficult to resolve experimentally and expensive to capture with first–principles calculations. In this work, we develop a hybrid molecular dynamics/Monte Carlo (MD/MC) framework, using a deep learning potential benchmarked against first-principles density functional theory/Monte Carlo (DFT/MC) calculations, to analyze supercell-size effects (from 4 × 4 × 1 to 12 × 12 × 1) on chemical ordering in two representative non-terminated carbide HE MXenes with octahedral coordination: (Ti0.25V0.25Cr0.25Mo 0.25)4C3 and (Ti0.25V0.25Nb0.25Mo 0.25)4C3. We find that large supercells are necessary to accurately capture in-plane chemical ordering, whereas out-of-plane ordering is insensitive to supercell size. Large solute clusters also induce localized stress fields that may significantly influence mechanical behavior. This study offers a validated computational framework to predict chemical ordering in chemically complex 2D materials.

Proceedings Inclusion?

Planned:

Keywords

Computational Materials Science & Engineering, Modeling and Simulation, Machine Learning

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