With the current advancement in technology and the growing need to adopt sustainable development across all sectors of the economy, there is an increasing need for novel materials that can sustainably meet the desired functions. Nevertheless, the cycle of discovering new materials that typically involves research, initial computational predictions, design fabrication and commercialization has several inefficiencies. First, this entire process often takes decades and is not practical. Second, much of the knowledge and information available about the materials cycles and development exists in isolated data silos that hinder the flow and sharing of information. Lastly, the evaluation of materials is often conducted in a singular fashion primarily based on human-driven compositional decisions and data analysis. This approach is expensive and often marred with errors.
The experimental research and development of materials have been significantly outpaced by technological advances. Regardless, increasing materials experimentation rates is still considered fundamental in improving materials research. It involves automation, parallelization and miniaturization of the crucial steps in material research, such as computation, processing synthesis, characterization and data analysis. Besides, high-throughput techniques have been utilized to accelerate the discovery of novel functional materials. However, these techniques are often limited to 2D materials and fail to capture the bulk material and microstructural behaviors that are vital in developing structural materials. When used for 3D materials, the additional complications in the materials experimentation process lead to bottlenecks, thus limiting their application. Therefore, developing an integrated closed-loop high-throughput process for developing bulk alloy materials is highly desirable.
To overcome the aforementioned limitations, Professor Kenneth Vecchio’s research team from the University of California San Diego developed a High-Throughput Rapid Experimental Alloy Development (HT-READ) methodology to enable research and discovery of new alloy materials. This methodology consists of an integrated and closed-loop process for screening materials, inspired by modern automation innovations and broad chemical assays. The connection between the compositions and material properties was established via an artificial intelligence agent. The work is currently published in the journal, Acta Materialia.
The authors showed that leveraging additive manufacturing (3D printing), novel sample library and computational screening could benefit rapid high-throughput development of structural alloy materials. The sample library facilitated rapid fabrication and automated characterization of properties, microstructures and phases of structural alloys, while the application of additive manufacturing enabled the rapid fabrication of bulk samples, where microstructures are important for determining properties. The sample library design and specifications facilitated rotational automation with high flexibility and adaptability to accommodate various equipment to achieve different design goals.
By assigning the sample libraries with unique identifiers that were stored to make the sample data persistent, it was possible to prevent the loss of institutional knowledge. This approach could also result in rapid tuning and validation of various computations models used in materials research. Due to the high dependency of structural alloys on their microstructures, microstructural engineering was considered an integral part of the HT-READ process. Furthermore, the HT-READ process was used to validate new alloy compositions and properties, reporting a 100 MPa increase in ultimate tensile strength and 50% increase in yield strength with good ductility, for a modified Alloy 625 material.
In summary, an integrated HT-READ process was presented for developing bulk alloy materials. It is a general framework combining computational identification of potential candidate materials, sample library fabrication through multiple tests and processing paths and high-throughput analysis of the candidate materials. New experimental data was leveraged either by pursuing new design objectives or through subsequent iterations. In a statement to Advances in Engineering, Professor Kenneth Vecchio noted that the HT-READ is a promising route for the efficient and economical discovery of new functional materials.
Vecchio, K., Dippo, O., Kaufmann, K., & Liu, X. (2021). High-throughput rapid experimental alloy development (HT-READ). Acta Materialia, 221, 117352.