Active Knowledge Extraction from Cyclic Voltammetry

12 May 2021, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


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 settings to obtain such curve are not known \textit{a priori}.
In this paper, an active search framework is defined to accelerate identification of settings that enable knowledge extraction from CV experiments.
Towards this goal, a function space representation of CV responses is used in combination with Bayesian Model Selection (BMS) method to efficiently label the response to be either \textit{S-shape} or not \textit{S-shape}.
Using an active search with BMS oracle, we report a linear target identification in a 6-dimensional design space (comprising 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 etc.


Accelerated Catalyst Discovery
Gaussian Processes
Bayesian model selection
Active learning techniques
cyclic voltammetry


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