A Computational Framework for the Process and Chemistry Co-Design of Refractory Alloys for Additive Manufacturing

Solvus Global will partner with Texas A&M University (TAMU) as the Small Business Technology Transfer (STTR) Research Institute. Our team will use a combination of practical materials sourcing and manufacturing knowledge, (existing) physics-based models, thermodynamics and kinetics calculations, Machine-Learning (ML) models derived from large in-house-developed datasets, physics-augmented ML models, as well as a combination of high-throughput (HTP) and low-throughput (LTP) synthesis-characterization-testing loops in order to navigate the refractory alloy space and achieve optimal designs. We have already validated most of the ML/simulation tools that we will be using. We have already demonstrated the deployment of hybrid computational / experimental iterative loops to discover optimal alloys under constraints. The team will deliver: Phase I – Demonstration of an ICME-inspired algorithm for the rapid exploration of vast refractory alloy spaces capable of accounting for multiple key metrics simultaneously including performance under extreme environments, processability via additive manufacturing (AM), costs, and supply chain constraints, along with a down-selected list of alloys with optimal performance-printability-supply chain robustness. Phase II – Fabricate, characterize, and test coupons of the down-selected alloys and provide a full assessment of performance and fabrication protocols with emphasis on technical criteria for scale-up. Demonstrated alloys capable of operating at elevated temperatures while being suitable for AM processing with robust supply chains. Phase III – a scaled-up protocol for the demonstration of application-relevant components. Approved for Public Release | 25-MDA-12052 (27 Mar 25)