Data‐Driven Analysis of High‐Throughput Experiments on Liquid Battery Electrolyte Formulations: Unraveling the Impact of Composition on Conductivity

• Published in: Chemistry methods 2022-09, Vol.2 (9), p.n/a
• Main Authors: Narayanan Krishnamoorthy, Anand, Wölke, Christian, Diddens, Diddo, Maiti, Moumita, Mabrouk, Youssef, Yan, Peng, Grünebaum, Mariano, Winter, Martin, Heuer, Andreas, Cekic‐Laskovic, Isidora
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Automation; Conductivity; Data driven analysis; Datasets; Electrodes; Electrolytes; Experiments; High-throughput screening; Ionic conductivity; Liquid electrolyte; Lithium; Machine learning; Solvents
• ISSN: 2628-9725; 2628-9725
• DOI: 10.1002/cmtd.202200008

A specially designed high‐throughput experimentation facility, used for the highly effective exploration of electrolyte formulations in composition space for diverse battery chemistries and targeted applications, is presented. It follows a high‐throughput formulation‐characterization‐optimization chain based on a set of previously established electrolyte‐related requirements. Here, the facility is used to acquire large dataset of ionic conductivities of non‐aqueous battery electrolytes in the conducting salt‐solvent/co‐solvent‐additive composition space. The measured temperature dependence is mapped on three generalized Arrhenius parameters, including deviations from simple activated dynamics. This reduced dataset is thereafter analyzed by a scalable data‐driven workflow, based on linear and Gaussian process regression, providing detailed information about the compositional dependence of the conductivity. Complete insensitivity to the addition of electrolyte additives for otherwise constant molar composition is observed. Quantitative dependencies, for example, on the temperature‐dependent conducting salt content for optimum conductivity are provided and discussed in light of physical properties such as viscosity and ion association effects. A scalable data‐driven workflow is proposed, to predict ionic conductivities of non‐aqueous battery electrolytes based on linear and Gaussian regression, considering a dataset acquired from specially designed high‐throughput electrolyte formulation to conductivity measurement sequence. Deeper insight into compositional effects is gained from a data‐driven analysis using surrogate models with physically interpretable terms from a generalized Arrhenius analysis.