(4hv) Integrated Computational Approach for Accelerated Materials Discovery and Advancement

The rapid development of new functional materials for technological innovation warrants accelerated discovery and advancement. While the ab initio approach can leverage the former, the latter is an interdisciplinary and multiscale problem, requiring an understanding of the science at the microscopic and macroscopic levels. An appealing solution to this problem is cross-disciplinary simulation across several characteristic lengths and timescales. I thus aim to establish an Integrated Computational Materials Lab to facilitate novel materials development in four major areas—Photovoltaic (PV), Photoelectrochemical (PEC), and antibiofouling coating, and ultra-high temperature applications using ab initio and multiscale simulations, with predictive data-driven and analytical property models.

Despite many predictions, the successful discovery of the predicted materials and the correspondence between theory and experiment are rare. My work will demonstrate the pathway for streamlined ab initio predictions, leading to the accelerated discovery for both conventional (ordered) and high-entropy (ordered) systems, evidenced by the synthesis of CuBiW₂O₈ and high-entropy carbides following my predictions.

Designing a new material warrants material insights. For example, zwitterions are promising candidates for salt-resistant antifouling coating due to their strong hydration. Designing new zwitterions would thus require understanding their hydration mechanism at the molecular level. This work will show how multiscale simulations can complement our understanding of zwitterionic hydration to design new salt-resistant zwitterionic materials.

The presence of defects is evident during the growth of a material, which can significantly change the energy landscape and, consequently, the properties. Moreover, the unwanted secondary phase can co-exist depending on the chosen growth conditions. Therefore, controlled growth is thus necessary to achieve the desired performance. I will demonstrate how high-fidelity thermodynamic modeling can help find effective growth conditions for achieving higher solar conversion efficiency for Sn-O, SnWO₄, Cu₂SnZnS₄, and Cu₂SnZn(S,Se)₄, using chemical potential landscape analysis and ab initio defect energetics. This thermodynamic modeling can be extended to other systems in general.

Research Interests: inorganic and organic-inorganic hydride halide perovskite-based photovoltaics, photocatalytic water splitting, nanoparticles-protein interactions, biosensor, antibiofouling coating, and ultra-high temperature applications.

Teaching Interests: Chemical engineering fundamentals, computational chemistry, statistical mechanics, thermodynamics, numerical methods, computer programming.