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

Material acceleration platforms (MAPs) aim at accelerating the discovery of functional materials by combining simulation-assisted pre-screening of potential candidate materials with fast property evaluation using self-driving labs (SDLs) enabling an early upscaling and assessment of device level properties. For the development of corrosion protection technologies or in energy applications, a key challenge lies in identifying property descriptors that reliably predict long-term stability while remaining compatible with accelerated workflows. Conventional corrosion tests for long-term performance remain indispensable but include procedures that can last from hours to months which renders them unsuitable for early-stage screening within large chemical and microstructural design spaces. Consequently, there is a critical need for time‑efficient electrochemical measurements that retain predictive value to determine candidate materials and can be seamlessly integrated into automated platforms. This study presents a comparative evaluation of the corrosion properties of four different alloys (CrCoNi, FeCrNi, CrMnFeCoNi, and AISI 304) in 3.5 wt% NaCl at pH 7.0, with the objective of developing a minimal yet informative electrochemical sequence suitable for SDL deployment. We outline a systematic approach to designing electrochemical proxy experiments: first, by identifying the corrosion processes most relevant to the target application; second, by assessing candidate techniques with respect to their diagnostic value relative to experimental time; and finally, by refining the sequence based on data interpretability and operational flexibility. For the investigated alloys and conditions, chronopotentiometry was the most effective method both in terms of the property differentiation and execution time.

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Publication Details

Journal
Analytical and Bioanalytical Chemistry
Published
2026-06-19
DOI
https://doi.org/10.1007/s00216-026-06609-9
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.00

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article

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

Ahmad Rashid Hazem, Mert Ozan, Annica Heyne, Ozlem Ozcan
Analytical and Bioanalytical Chemistry
Machine Learning in Materials Science
article

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

Ahmad Rashid Hazem, Mert Ozan, Annica Heyne, Ozlem Ozcan
article en

Abstract

Material acceleration platforms (MAPs) aim at accelerating the discovery of functional materials by combining simulation-assisted pre-screening of potential candidate materials with fast property evaluation using self-driving labs (SDLs) enabling an early upscaling and assessment of device level properties. For the development of corrosion protection technologies or in energy applications, a key challenge lies in identifying property descriptors that reliably predict long-term stability while remaining compatible with accelerated workflows. Conventional corrosion tests for long-term performance remain indispensable but include procedures that can last from hours to months which renders them unsuitable for early-stage screening within large chemical and microstructural design spaces. Consequently, there is a critical need for time‑efficient electrochemical measurements that retain predictive value to determine candidate materials and can be seamlessly integrated into automated platforms. This study presents a comparative evaluation of the corrosion properties of four different alloys (CrCoNi, FeCrNi, CrMnFeCoNi, and AISI 304) in 3.5 wt% NaCl at pH 7.0, with the objective of developing a minimal yet informative electrochemical sequence suitable for SDL deployment. We outline a systematic approach to designing electrochemical proxy experiments: first, by identifying the corrosion processes most relevant to the target application; second, by assessing candidate techniques with respect to their diagnostic value relative to experimental time; and finally, by refining the sequence based on data interpretability and operational flexibility. For the investigated alloys and conditions, chronopotentiometry was the most effective method both in terms of the property differentiation and execution time.

Analytical and Bioanalytical Chemistry
Federal Institute For Materials Research and Testing (DE)
Bundesanstalt für Materialforschung und -Prüfung
Affordable and clean energy
Openalex Percentile: Top 15%
Machine Learning in Materials Science
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