Towards automated design of corrosion resistant alloy coatings with an autonomous scanning droplet cell

TL;DR

This paper introduces an autonomous platform for alloy coating design that combines real-time electrochemical testing with machine learning to optimize corrosion resistance in multicomponent alloys.

Contribution

It develops a novel autonomous scanning droplet cell platform integrating machine learning for on-demand alloy synthesis and characterization, advancing automated corrosion-resistant material discovery.

Findings

Successful multi-objective alloy optimization

Insights into structural and chemical factors affecting corrosion resistance

Demonstration of autonomous platform's effectiveness in materials discovery

Abstract

We present an autonomous scanning droplet cell platform designed for on-demand alloy electrodeposition and real-time electrochemical characterization for investigating the corrosion-resistance properties of multicomponent alloys. Automation and machine learning are currently driving rapid innovation in high throughput and autonomous materials design and discovery. We present two alloy design case studies: one focusing on a multi-objective corrosion resistant alloy optimization, and a case study highlighting the complexity of the multimodal characterization needed to provide insight into the underlying structural and chemical factors that drive observed material behavior. This motivates a close coupling between autonomous research platforms and scientific machine learning methodology that blends mechanistic physical models and black box machine learning models. This emerging research area presents new opportunities to accelerate materials synthesis, evaluation, and hence discovery and design.