Combining High-Throughput Electrochemistry and Data-Driven Experimentation for Digital Discovery of Si-Ge-Sn Thin-Film Battery Anode Materials
Authors: Sanin Aleksei , Flowers Jackson K. , Stein Helge Sören 
Published: December 2024

The urgent global push towards decarbonization underlines the need for accelerated development of innovative energy storage technologies. In this context, the integration of data-driven research methodologies, such as machine learning and experimental automation, is emerging as a transformative strategy for expediting the discovery and optimization of electrochemical energy storage materials. This research work presents a pioneering approach within experimental materials science, combining automated thin-film experimentation with machine learning algorithms to significantly accelerate the development of novel battery materials while minimizing research time, resource consumption and overall costs.

This contribution shows an autonomous materials acceleration platform developed and tailored for the rapid discovery of novel thin-film anode materials. This closed-loop system integrates high-throughput electrochemistry with sequential learning algorithms to dynamically determine the course of subsequent experiments. This data-driven optimization has facilitated rapid identification of optimal material compositions, resulting in a nearly 20-fold increase in the discovery speed and a more than 30% improvement in gravimetric energy density compared to conventional graphite anodes.

Our platform incorporates thin-film material libraries synthesized via magnetron co-sputtering with a chemical composition gradient. These libraries are thoroughly electrochemically characterized in an argon glovebox using a scanning droplet cell (SDC) setup. This setup consists of a millimeter-scale three-electrode electrochemical cell, which is controlled by a potentiostat and driven by high-precision motors, with the capability of electrolyte exchange using a syringe pump. Additionally, the setup integrates a machine learning unit and an on-the-fly analysis module, all driven by a hierarchical orchestration framework. This framework facilitates real-time analysis of impedance spectra using physically interpretable equivalent circuits, calculating the specific capacity, energy density, and other critical parameters of electrode performance. In addition, the platform integrates additional high-throughput techniques such as µ-XRF and XPS for bulk and surface chemical analysis, respectively, and Raman spectroscopy for structural evaluation, enhancing our understanding of material properties and performance metrics.

The novel Si-Ge-Sn ternary alloy system selected for this study, combines the advantages of the highest theoretical capacity of silicon, as well as mitigation of volume expansion during cycling and enhanced electrical conductivity of germanium and tin, making them suitable as fast-charging anodes. The material libraries were sputtered on 4" wafers, with a focus on optimizing the deposition process to achieve a wide compositional spread, high deposition yield, and amorphous phase across the material library, expected to enhance performance as battery anodes. Employing Bayesian optimization with a Gaussian process regressor, the material with the highest expected capacity was predicted after only 4 measurements, and the best performing material was identified after 14 trials. High-throughput Raman results highlighted shifts in Ge-Ge and Si-Si band positions that correlated with chemical composition changes, while XPS identified surface oxides that affect battery performance, explaining the discrepancy between theoretical and experimental results. The best materials have been selected for the scalability and long-term cycling tests at 1C dis-/charge rates, and their performance has been compared with the performance of pure elements. The results have validated the practicality of the high-throughput electrochemical experimentation approach, confirming the viability of the materials acceleration platform.

In conclusion, this research demonstrates a transformative approach in battery material development by integrating machine learning with high-throughput characterization techniques. The autonomous materials acceleration platform developed herein significantly expedites the discovery and optimization of novel anode materials. This work highlights the pivotal role of AI and data-driven methodologies in advancing electrochemical energy storage technologies, paving the way for rapid, sustainable, and efficient advances, crucial for accelerating the discovery of novel solutions for advanced energy storage.