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

Daniel Persaud, Mike Werezak, Mark Xu, Melyne Zhou, Frank Benkel, Xin Pang, Vahid Attari, Brian DeCost, Ashley Dale, Nicholas Senior, Gabriel Birsan, Jason Hattrick-Simpers

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.

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

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.
Paper Structure (10 sections, 6 figures)

This paper contains 10 sections, 6 figures.

Table of Contents

  1. Introduction
  2. Methods
  3. Hardware
  4. Control Software
  5. Adaptive Experimentation Framework
  6. Materials and Sample Preparation
  7. Results and Discussion
  8. Corrosion Validation
  9. Building Information-Rich Stability Diagrams Autonmously
  10. Conclusions

Figures (6)

  • Figure 1: Key hardware subsystems of the MAP-E platform.
  • Figure 2: Schematic of the MAP-E software architecture, illustrating the three primary layers: application server, experiment execution engine, and instrument driver libraries.
  • Figure 3: Illustration of the resource allocation and scheduling framework within the experiment execution engine, demonstrating parallel operation of multiple electrochemical cells while managing shared resources like the mixing tank. In this example, separate experiments are denoted by different colors and only two cells are shown for clarity.
  • Figure 4: Results from the ASTM G61 replication experiments with the MAP-E.
  • Figure 5: High-level workflow diagram of the autonomous experimental workflow for constructing pH–Cl$^-$ stability diagrams using the MAP-E platform. The red boxes indicate steps that can primarily be associated with the hardware, the green boxes indicate software-driven steps, and the purple boxes indicate the adaptive modeling components.
  • ...and 1 more figures