I am a Staff Scientist at Factorial Energy, where I work at the intersection of computational chemistry, non-equilibrium thermodynamics, and machine learning to understand and improve electrolytes for next-generation batteries.
My research centers on thermodynamic properties of electrolytes and the electrode-electrolyte interface — how ions are solvated, how they move, and how the interface forms and evolves. I am particularly drawn to non-equilibrium transport phenomena: understanding ionic conductivity through the lens of Onsager theory, correlated ion motion in concentrated electrolytes, and the physical origins of electrochemical performance.
In recent years I have also been developing electrochemistry-driven data science methods — combining signal analysis with machine learning to predict cycle life and support quality control for solid-state batteries.
My background is in computational physics (TDDFT and quantum optimal control theory, Ph.D. National Taiwan University). I did my postdoc in Chemical Engineering at Stanford University (2021–2023), which is where I made the jump from electrons to ions. The physics turned out to transfer surprisingly well.
My work has appeared in Nature Energy, Nature Communications, Physical Review Letters, and Chemical Science. Full list on Google Scholar.
This website collects my research notes, simulation workflows, and thoughts on scientific computing.