High-Throughput Rapid Experimental Alloy Development (HT-READ)

TL;DR

HT-READ is a comprehensive, automated framework that accelerates alloy discovery by integrating computational predictions, high-throughput fabrication, and AI-driven analysis in a closed-loop process.

Contribution

The paper introduces a novel, general framework for rapid alloy development combining automation, AI, and high-throughput experimentation to improve efficiency and data management.

Findings

Automated alloy screening process reduces development time.

AI identifies key composition-property relationships.

Persistent data storage enhances knowledge retention.

Abstract

The current bulk materials discovery cycle has several inefficiencies from initial computational predictions through fabrication and analyses. Materials are generally evaluated in a singular fashion, relying largely on human-driven compositional choices and analysis of the volumes of generated data, thus also slowing validation of computational models. To overcome these limitations, we developed a high-throughput rapid experimental alloy development (HT-READ) methodology that comprises an integrated, closed-loop material screening process inspired by broad chemical assays and modern innovations in automation. Our method is a general framework unifying computational identification of ideal candidate materials, fabrication of sample libraries in a configuration amenable to multiple tests and processing routes, and analysis of the candidate materials in a high-throughput fashion. An artificial intelligence agent is used to find connections between compositions and material properties. New experimental data can be leveraged in subsequent iterations or new design objectives. The sample libraries are assigned unique identifiers and stored to make data and samples persistent, thus preventing institutional knowledge loss.