Publication date: 22nd July 2026
Materials Acceleration Platforms (MAPs) are emerging as a powerful paradigm for closing the loop between materials synthesis, characterization, data analysis, and decision-making, enabling faster and more autonomous materials discovery. In this talk, we present an electrochemistry-focused MAP developed for the accelerated design of functional interfaces relevant to energy conversion, electrocatalysis, and corrosion protection. The platform integrates automated liquid handling, parallelized electrochemical experimentation, standardized workflows, and machine learning-driven experiment planning within a unified digital infrastructure.
Central to the approach is the generation of high-quality, FAIR-compatible datasets with structured metadata that support active learning, inverse design, and reproducible experimentation. Electrochemical descriptors are employed as application-specific proxy metrics and correlated with long-term performance indicators, enabling rapid identification of promising materials while reducing experimental burden. To efficiently navigate complex design spaces, we combine Bayesian optimization, Pareto-based multi-objective analysis, and alternative machine-learning strategies, highlighting how algorithm performance depends strongly on the underlying materials system and optimization objective.
Through case studies on electrodeposited metallic and alloy thin films, we demonstrate accelerated optimization of corrosion resistance and catalytic activity, while uncovering processing-structure-property relationships that guide materials design. The presentation will summarize the design and construction phases of our MAPs, their constituent modules, and workflows. It will also include deep dives into examples on Pareto optimization and evaluation of proxy electrochemical experiments for MAP-deployment. Beyond individual applications, the presentation will also discuss practical challenges that remain critical for realizing fully autonomous discovery workflows, including instrument interoperability and integration of legacy characterization techniques.
