A high-throughput experimentation platform for data-driven discovery in electrochemistry

Creators: Lin, Dian-Zhao¹; Pan, Kai-Jui¹; Li, Yuyin^{2, 3}; Musgrave, Charles B., III³; Zhang, Lingyu¹; Jayarapu, Krish N.¹; Li, Tianchen¹; Tran, Jasmine Vy¹; Goddard, William A., III³; Luo, Zhengtang²; Liu, Yayuan¹

1. Johns Hopkins University
2. Hong Kong University of Science and Technology
3. California Institute of Technology

Abstract

Automating electrochemical analyses combined with artificial intelligence is poised to accelerate discoveries in renewable energy sciences and technologies. This study presents an automated high-throughput electrochemical characterization (AHTech) platform as a cost-effective and versatile tool for rapidly assessing liquid analytes. The Python-controlled platform combines a liquid handling robot, potentiostat, and customizable microelectrode bundles for diverse, reproducible electrochemical measurements in microtiter plates, minimizing chemical consumption and manual effort. To showcase the capability of AHTech, we screened a library of 180 small molecules as electrolyte additives for aqueous zinc metal batteries, generating data for training machine learning models to predict Coulombic efficiencies. Key molecular features governing additive performance were elucidated using Shapley Additive exPlanations and Spearman's correlation, pinpointing high-performance candidates like cis -4-hydroxy- d -proline, which achieved an average Coulombic efficiency of 99.52% over 200 cycles. The workflow established herein is highly adaptable, offering a powerful framework for accelerating the exploration and optimization of extensive chemical spaces across diverse energy storage and conversion fields.

Copyright and License

© 2025 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).

Acknowledgement

We thank I. G. Kevrekidis, N. Evangelou, and A. Georgiou for helpful discussions in ML. This work was performed (in part) at the Materials Characterization and Processing Center in the Whiting School of Engineering at the Johns Hopkins University.

Funding

This work was supported by the Arnold and Mabel Beckman Foundation through a Beckman Young Investigator Award and the Johns Hopkins University Ralph O’Connor Sustainable Energy Institute through a SPARK award. Yu.L., C.B.M., and W.A.G. acknowledge support from the Liquid Sunlight Alliance, which is supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Fuels from Sunlight Hub under award no. DE-SC0021266. This research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under contract no. DE-AC02-05CH11231 using NERSC award BES-ERCAP0024109.

Data Availability

All code used for controlling the AHTech platform, quantitative data analysis, and ML model training have been deposited in the database Zenodo (https://doi.org/10.5281/zenodo.14602859) and can also be found on GitHub (https://github.com/Liu-Lab-JHU/AHTech). All data needed to evaluate the conclusions of the paper are present in the paper and/or the Supplementary Materials.

Supplemental Material

Figs. S1 to S14; Tables S1 to S27; Notes S1 and S2; Legends for movies S1 and S2; Legends for data S1 and S2; References (PDF).

Movies S1 and S2 (ZIP).

Data S1 and S2 (ZIP).

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