|About this Abstract
||3rd World Congress on High Entropy Alloys (HEA 2023)
||High Throughput Exploration of High Entropy MXenes using Neural Network Potentials
||Mohammed Wasay Mudassir, Sriram Goverapet Srinivasan, Mahesh Mynam, Beena Rai
|On-Site Speaker (Planned)
||Mohammed Wasay Mudassir
MXenes, a class of 2D materials of the general formula Mn+1XnTy(M = early ‘d’ block metal, X = C/N, T= O/OH/F) have shown promise as efficient materials for various applications ranging from electrocatalysis to battery anodes and supercapacitors. Configurational entropy driven stabilization was leveraged to synthesize multi-metallic MXenes recently. To explore the vast configurational and compositional space, density functional theory calculations were used to develop a neural network potential for Ti,Nb,V and Mo containing carbide MXenes. The potential, that was trained against equation of state and formation energies data, had a low RMSE of 6meV/atom in energy and 44meV/Å in forces. MC/MM simulations were carried out with this potential to construct the convex hull and identify stable MXenes of (TiaVbMocNd)Cn(a+b+c+d=n+1) stoichiometry. The MXenes at the vertices of the hull were further characterized in terms of the SRO parameters, inter-layer segregation parameters as well as the distribution of atomic neighborhoods.
||Planned: Metallurgical and Materials Transactions