Qualification of proxy experiments for accelerated electrochemical testing in self-driving labs

Area of Science:

• Materials Science
• Electrochemistry
• Corrosion Engineering

Background:

• Material Acceleration Platforms (MAPs) integrate simulations and self-driving labs (SDLs) for rapid functional material discovery.
• Predicting long-term material stability, especially for corrosion protection and energy applications, requires efficient property descriptors compatible with accelerated workflows.
• Traditional long-term corrosion tests are time-prohibitive for early-stage screening in extensive material design spaces.

Purpose of the Study:

• To develop a minimal and informative electrochemical testing sequence for SDL deployment in materials corrosion assessment.
• To identify time-efficient electrochemical methods that retain predictive value for material stability.
• To comparatively evaluate the corrosion properties of four distinct alloys (CrCoNi, FeCrNi, CrMnFeCoNi, AISI 304) in a standard saline environment.

Main Methods:

• A systematic approach to designing electrochemical proxy experiments was employed.
• Key corrosion processes relevant to the application were identified.
• Candidate electrochemical techniques were assessed for diagnostic value versus experimental duration.
• The sequence was refined based on data interpretability and operational flexibility.

Main Results:

• Chronopotentiometry emerged as the most effective method for differentiating alloy corrosion properties within the tested conditions.
• This technique demonstrated superior performance in terms of both diagnostic capability and execution time compared to other evaluated methods.
• The study successfully identified a concise electrochemical sequence suitable for automated deployment in SDLs.

Conclusions:

• Chronopotentiometry is a highly effective and time-efficient electrochemical technique for the accelerated screening of material corrosion properties.
• The developed methodology enables the integration of predictive corrosion assessment into self-driving laboratory workflows.
• This approach facilitates faster identification of promising materials for applications requiring long-term stability, such as corrosion protection.