| About this Abstract |
| Meeting |
TMS Specialty Congress 2025
|
| Symposium
|
8th World Congress on Integrated Computational Materials Engineering (ICME 2025)
|
| Presentation Title |
High-Throughput Screening of Ti-V-Nb-Mo Carbide MXenes Using Machine Learned Potentials and Their Assessment as Catalysts for Hydrogen Evolution Reaction |
| Author(s) |
Mohammed Wasay Mudassir, Sriram Goverapet Srinivasan, Mahesh Mynam, Beena Rai |
| On-Site Speaker (Planned) |
Mohammed Wasay Mudassir |
| Abstract Scope |
MXenes, a versatile class of 2D materials, have shown great potential as catalysts for the hydrogen evolution reaction (HER). The recent discovery of high entropy (HE)-MXenes has significantly broadened the compositional landscape, suggesting potential diverse candidates with enhanced stability and functionality. Leveraging atomistic modeling, we systematically explored this vast design space by developing a Neural Network Potential (NNP) trained in an active learning fashion on Density Functional Theory (DFT) data for (TixVyNbzMop)n+1Cn MXenes (where x+y+z+p=1; n=1,2,3). This NNP was utilized to identify thermodynamically stable compositions from a vast number of HE-MXenes and to examine how the arrangement of transition metals within and across layers influences stability. Additionally, we assessed the catalytic HER performance of these stable MXenes by calculating the hydrogen adsorption energy, ΔG(*H) using DFT. In this presentation, I will discuss the NNP development workflow and study findings, highlighting new synthesizable HE-MXenes with catalytic potential for HER. |
| Proceedings Inclusion? |
Undecided |