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

Published

June 21, 2022

Author(s)

Brian DeCost, Howie Joress, Suchismita Sarker, Apurva Mehta, Jason Hattrick-Simpers

Abstract

We present an autonomous scanning droplet cell platform designed for on-demand alloy electrodeposition and real-time electrochemical characterization. Automation and machine learning are currently driving rapid innovation in high-throughput and autonomous materials design and discovery. We present two alloy design vignettes: one focusing on a multi-objective corrosion resistant alloy optimization and a 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. Finally, we reflect on our early efforts in on-demand alloy deposition, highlighting some of the challenges. This emerging research area presents new opportunities to accelerate materials synthesis, evaluation, and hence discovery and design.

Citation

JOM Journal of the Minerals Metals and Materials Society

Volume

74

Issue

8

Pub Type

Journals

Download Paper

https://doi.org/10.1007/s11837-022-05367-0

Local Download

Citation

DeCost, B. , Joress, H. , Sarker, S. , Mehta, A. and Hattrick-Simpers, J. (2022), Towards automated design of corrosion resistant alloy coatings with an autonomous scanning droplet cell, JOM Journal of the Minerals Metals and Materials Society, [online], https://doi.org/10.1007/s11837-022-05367-0, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=934422 (Accessed December 3, 2024)