Materials Acceleration Platform for Electrochemistry (MAP-E): a Platform for Autonomous Electrochemistry

TL;DR

MAP-E is an autonomous high-throughput platform that automates electrochemical experiments, enabling rapid, reproducible corrosion data collection to accelerate materials discovery and durability assessment.

Contribution

The paper introduces MAP-E, a novel autonomous system integrating robotics and machine learning for high-throughput electrochemical testing, reducing operator involvement and increasing data quality.

Findings

Reproducible corrosion measurements with low standard deviation.

Successful autonomous construction of pH-chloride stability diagrams.

Reduced operator involvement in corrosion testing processes.

Abstract

Corrosion testing is slow, labor-intensive, and sensitive to operator technique, limiting the generation of large, high-quality datasets for data-driven materials discovery. We introduce the Materials Acceleration Platform for Electrochemistry (MAP-E), an autonomous, high-throughput system capable of performing parallel electrochemical experiments. MAP-E integrates robotic liquid handling, sample transfer, and multi-channel potentiostatic control and extract corrosion metrics without human intervention. Validation against an ASTM G61-analog benchmark demonstrates reproducibility, with a standard deviation of 76 mV in pitting potential across 32 automated measurements. The platform was then employed to autonomously construct pH-chloride stability diagrams for 304 stainless steel using an uncertainty-driven sampling strategy on a Gaussian Process surrogate model. This approach reduces operator involvement and accelerates the exploration of environmental spaces. MAP-E establishes a framework for autonomous electrochemical experimentation, enabling generation of corrosion datasets that inform materials discovery, alloy design, and durability assessment in service environments.