My research combines atomistic simulations, rare-event sampling techniques, and machine learning to understand and predict materials behavior across multiple domains.
Investigating the structure, transport properties, and mechanisms of battery materials using physics-informed machine learning and enhanced sampling techniques. Recent work includes exploring Na storage mechanisms in disordered carbon, structural properties of battery electrolytes at extreme temperatures, and ion transport across interfaces.
My research employs a combination of classical molecular dynamics, advanced sampling techniques, and machine learning to predict and optimize the performance of next-generation battery materials. I focus on understanding the atomic-scale processes that govern ion insertion, extraction, and transport in electrode and electrolyte materials.
Exploring reactivity in aqueous solutions and at mineral-water interfaces using rare event theory and atomistic simulations. This includes studying ion solvation, nucleation pathways for mineral formation, and interfacial processes governing environmental and geological systems.
By combining advanced molecular dynamics simulations with rare event sampling techniques, I investigate complex geochemical processes that occur over extended time and length scales. My work helps explain mineral formation, dissolution, and transformation mechanisms that are critical for environmental remediation, CO2 sequestration, and geological evolution.
Investigating material behavior under extreme pressures and temperatures, including phase transitions in shock-compressed titanium and other metals. This work combines high-performance computing, machine learning potentials, and theoretical modeling to predict material response in extreme environments.
My research focuses on understanding how materials behave when subjected to conditions far from equilibrium, such as those experienced during shock compression, high-energy impacts, or in extreme planetary environments. By developing accurate computational models for these conditions, I can predict material performance in applications ranging from aerospace to nuclear energy.
Developing physics-informed machine learning models that bridge quantum mechanical accuracy with classical simulation efficiency. This research focuses on creating ML potentials for complex materials systems, uncertainty quantification in ML predictions, and interpretable AI approaches for materials discovery and design.
My work pioneers the integration of machine learning with traditional physics-based models to accelerate materials discovery and characterization. I develop neural network potentials that can match the accuracy of quantum mechanical calculations at a fraction of the computational cost, enabling large-scale simulations of complex materials systems while maintaining quantum-level accuracy.
My research leverages cutting-edge computational techniques to understand and predict material behavior at the atomic scale. I employ a multi-faceted approach that combines:
Through close collaboration with experimentalists, I develop models that not only explain observed phenomena but also guide new materials discovery and design.