Author: Haotian Chen
Engineering Research
Schmidt AI in Science Fellow Haotian Chen, along with his Science mentor Prof. Venkat Viswanathan and AI mentor Prof. Alexander Rodríguez are building the first differentiable electrochemistry framework to bridge the theory-experimental gap in electrochemistry. Electrochemistry simulations are made end-to-end differentiable to obtain gradients of physical processes for learning and optimization. More importantly, differentiable electrochemistry enables first-principles discovery of essential parameters like rate constants, reorganization energy, or transfer coefficients from experiments, to probe kinetics, understand mechanics, and guide experimental design by optimizing these parameters.
Haotian has developed differentiable simulations for all major modes of mass transport: diffusion, migration and convection and both the macroscopic Butler-Volmer kinetics and the microscopic Marcus-Hush-Chidsey kinetics. In addition, it was compatible with coupled nonlinear electrochemical reactions (EC or CE reactions), and both semi-infinite and thin-layer boundary conditions. Compared with data-driven machine learning, differentiable simulations are significantly more efficient, interpretable and accountable. Initial application shows 100x training speedup compared with a neural network surrogate model. Haotian and his mentors believe that differentiable simulation will be the new paradigm of scientific machine learning.
Haotian is actively revising his manuscript with his mentors and will be released on a preprint server in due course. Working as a team, they are also actively exploiting opportunities to build a more accurate and universal battery management system for electric aviation.