(4ob) Accelerating Sustainable Energy Solutions through Data Science and Simulations in Synergy with Experiments

Decarbonization of our future society requires widespread sustainable utilization of energy at a global scale. To achieve carbon neutrality, advanced technologies of energy conversion and storage need to be developed to compensate the intermittent nature of renewable energy sources, especially solar and wind. A holistic approach, seamlessly integrating advanced energy storage and conversion technologies, stands at the forefront of this quest. Central to overcoming these challenges are domain-agnostic methodologies that bridge the gap between these two critical areas. In this context, computational simulations and data science techniques emerge as powerful tools, offering the potential to accelerate the development of these technologies in synergy with experiments. Leveraging these methodologies, my current research endeavors are to usher in a new era of innovation in electrolyte discovery for both next-generation batteries such as lithium metal batteries and the enhancement of electrochemical carbon dioxide reduction (CO₂R) reduction selectivity. Employing state-of-the-art machine learning (ML) and data science techniques, I analyze extensive datasets compiled from the scientific literature to identify promising, yet unexplored, electrolyte candidates. These candidates undergo rigorous experimental validation in our lab and computational verification. Building on the success of these forward-design models, my research has expanded to explore Bayesian optimization for the development of anode-free batteries, showcasing our adaptive approach to discovering next-generation battery materials. For CO₂R electrolyzers, I employ density functional theory (DFT) and molecular dynamics (MD) simulations to propose simple, yet effective, descriptors based on bulk electrolyte solvation structure. These descriptors are instrumental in pinpointing electrolytes experimentally proven to selectively facilitate either hydrogen evolution reaction (HER) or CO₂R. Through a synergistic application of big data analysis, deep learning, and computational chemistry, closely linked with experiments, my research aims to address the dual challenges of efficient energy storage and sustainable fuel production.

Research Interests

As I transition towards an independent academic career, my ambition is to lead a research laboratory at the intersection of computational simulations and data science. This lab will focus on harnessing the capabilities of simulations at different scales, i.e., density functional theory, molecular dynamics, kinetic Monte Carlo simulations, and advanced data analytics to expedite the discovery of innovative materials for energy conversion and storage. Beyond acceleration, our aim is to deepen our understanding of the fundamental phenomena underpinning these technologies. My educational (master’s and PhD) and postdoctoral journey has equipped me with a robust foundation in electrochemistry, thermodynamics, statistical mechanics, computational simulations, and machine learning, complemented by extensive collaborations with experimentalists to align simulation insights with experimental observations.

Leveraging the synergistic approach of data science and simulations, tested, and refined through my postdoctoral work alongside experimental investigations, my goal is to pioneer the discovery of novel solid-state and polymer electrolytes. In the realm of energy conversion, the application of computational chemistry and deep learning will identify optimal combinations of aqueous electrolytes and catalysts for critical industrial reactions, including CO₂ mitigation and nitrogen reduction. In silico screening and design stand as pillars for the swift advancement of these future-facing technologies.

My vision encompasses the integration of high-throughput atomistic simulations with artificial intelligence to navigate and elucidate the complex chemical space effectively. A particular focus will be on simulating interfaces crucial to the functionality of energy conversion and storage devices. We will utilize the simulation-generated insights to train various machine learning models, including forward and inverse design techniques. Additionally, large language models (LLMs) will play a key role in refining our research processes, ensuring a streamlined and innovative exploration of materials spaces for specific applications.

Teaching interests

Rooted in a deep commitment to mentorship, my teaching philosophy transcends the mere transfer of knowledge, aiming instead to ignite a passion for learning and apply theoretical concepts to real-world situations. I prioritize understanding each student's unique needs and backgrounds, striving to tailor my teaching methods accordingly. This approach is designed to cultivate a supportive, inclusive classroom environment that encourages critical thinking and independent growth. My diverse academic background – spanning chemistry from my undergraduate and master’s studies, to materials science during my PhD, and chemical engineering in my postdoctoral work – enables me to offer a comprehensive, mechanism-driven perspective across a variety of chemical engineering subjects, including thermodynamics, statistical mechanics, quantum mechanics, computational simulations, and data science. Drawing on my research focus on renewable energy, I am particularly enthusiastic about developing new courses that bridge the gap between fundamental theories and practical applications in electrochemical energy technologies. Such courses will not only enrich the curriculum but also equip the next generation of scientists and engineers with the knowledge and skills necessary to address the grand challenges of energy sustainability. My ultimate goal is to foster an educational environment where students are inspired to pursue innovation and make substantive contributions to the field of energy.