Research Projects

High-entropy transition metal diborides (HE-TMB2) are a promising class of materials known for their exceptional mechanical hardness, thermal stability, and oxidation resistance, making them ideal for applications in extreme environments such as aerospace and defense. However, the development of these materials is hindered by the vast compositional space and the lack of robust predictive frameworks to identify stable, single-phase compositions. This research addresses these challenges by proposing a novel computational framework to design HE-TMB2 materials, combining high-throughput density functional theory (DFT), machine learning (ML), and systematic development of descriptors based on the underlying physics in the materials. The primary objective of this project is to develop a methodology for estimating cation-anion interaction in HE-TMB2 systems and to establish binding energy as a reliable descriptor for predicting single-phase stability. Recent investigations into the binding energy analysis have demonstrated a substantial impact of elemental repulsion on the phase stability of transition metal-based ceramic systems. This research project aims to capitalize on these insights by developing an advanced methodology for the design and exploration of novel high-entropy transition metal borides (HE-TEMB2s). High-throughput DFT calculations will evaluate the binding energies of various cations and anions across relevant crystal lattices. These calculations will generate a comprehensive database of elemental interactions, which will be the foundation for designing predictive models. Variance in binding energies across cation pairs will be analyzed to develop descriptors that quantify repulsion and predict phase stability. This systematic approach will provide a robust framework to efficiently explore the vast compositional space. The central hypothesis is that binding energy can reliably predict phase stability and that the high configurational entropy in HE-TMB2 systems, coupled with optimized elemental combinations, enhances their functional properties. The research will develop compatibility maps based on binding energy descriptors and machine learning models to rapidly identify optimal cation combinations for stable HE-TMB2 compositions. Correlation analyses will be conducted to link these descriptors with other critical parameters such as valence electron concentration (VEC) and entropy formation ability (EFA), ensuring a holistic understanding of stability and property relationships. This research aims to enhance the understanding of high-entropy ceramics, specifically in predicting phase stability in HE-TMB2 materials. It will result in a comprehensive computational framework, open-source databases, and predictive tools to expedite material discovery, ultimately leading to improved structural materials for extreme conditions.