Welcome to my page! My work primarily focuses on the intersection of Materials Science (with a key focus in Soft Matter Physics), Computation, and Artificial Intelligence (AI) to accelerate the discovery and design of complex materials through theory, high-performance computing and machine learning that solve critical societal challenges.
Currently, I am working on several soft matter problems by developing efficient computational scattering methods to resolve spatially-dependent dynamics across multiple spatiotemporal scales, focusing on bridging the gap between molecular simulation and experimental scattering. A key component of the contribution is the development of a digital-twin platform for molecular scattering.
Key contribution:
- Refining experimental interpretation: elucidated the physical chemistry origins of the noncritical background term in scattering fits for liquid-liquid critical phenomenon, improving data accuracy and interpretability.
- Critical fluid modeling: solved a long-standing debate in molecular simulations for near-critical fluids by incorporating long-range dispersion, enabling high-fidelity modeling in these regimes.
- Nonequilibrium dynamics: characterized the molecular mechanisms behind order-disorder transitions and heterogeneous dynamics in shear-thickening colloidal suspensions, validated against experimental XPCS data.
- Software development: engineered several computational and AI-driven suites for autonomous nanomaterials design. These tools integrate machine learning with molecular modeling to automate the discovery of novel nanostructures.
