Active Knowledge Extraction from Cyclic Voltammetry

Abstract

Cyclic Voltammetry (CV) is an electro-chemical characterization technique used in an initial material screening for desired properties and to extract information about electro-chemical reactions. In some applications, to extract kinetic information of the associated reactions (e.g., rate constants and turn over frequencies), CV curve should have a specific shape (for example an S-shape). However, often the characterization settings to obtain such curve are not known a priori. In this paper, an active search framework is defined to accelerate identification of characterization settings that enable knowledge extraction from CV experiments. Towards this goal, a representation of CV responses is used in combination with Bayesian Model Selection (BMS) method to efficiently label the response to be either S-shape or not S-shape. Using an active search with BMS oracle, we report a linear target identification in a six-dimensional search space (comprised of thermodynamic, mass transfer, and solution variables as dimensions). Our framework has the potential to be a powerful virtual screening technique for molecular catalysts, bi-functional fuel cell catalysts, and other energy conversion and storage systems.