Improved Material Discovery through Leveraging Novel Modeling and Experimental Approaches

Improved Material Discovery through Leveraging Novel Modeling and Experimental Approaches is sponsored by NASA. This NASA SBIR topic encourages proposals emphasizing modeling (both physics-based and data-driven AI/ML), materials informatics, and advanced/novel experimentation to accelerate material development.

Improved Material Discovery through Leveraging Novel Modeling and Experimental Approaches Topic Number: AERO. 5. S26B Solicitation Number: SBIR_26B_P1 Solicitation Status: Closed NOTE: The Solicitations and topics listed on this site are copies from the various SBIR agency solicitations and are not necessarily the latest and most up-to-date.

For this reason, you should use the agency link listed below which will take you directly to the appropriate agency server where you can read the official version of this solicitation and download the appropriate forms and rules.

View Official Solicitation NASA aeronautics has wide-ranging material needs across multiple flight regimes (subsonic, supersonic, hypersonic) and multiple vehicles classes from more traditional commercial transport to emerging areas such as urban air mobility (UAM), electric vertical take-off and landing (eVTOL) and others.

To support these diverse applications, new materials are needed to support advances in propulsion, structures, and electrification, among others, to enable the high-performance vehicles of the future.

Advances in the associated manufacturing processes will also be critical for high-rate production, additive manufacturing of complex parts, etc. Rapid qualification and certification of new materials and processes will be crucial to facilitate the adoption and deployment of new technologies.

Acceleration of material development will require advances in characterization/testing as well as multiscale modeling that leverage physics-based as well as data-driven (AI/ML) approaches across multiple length and time scales, high-throughput computing, digital workflow/assistants, etc. NASA recently published a report, "Vision 2040: A Roadmap for Integrated, Multiscale Modeling and Simulation of Materials and Systems" [Ref.

1], which detailed a paradigm shift towards accelerated, fit-for-purpose material design. Proposals emphasizing modeling (both physics-based and data driven), materials informatics and advanced/novel experimentation which address gaps in that 2040 Vision are encouraged.

The range of topics could include data management [Ref 2], data analytics, machine learning [Ref 3], linkage and integration across spatiotemporal scales [Refs 4, 5], and high through-put experiments and characterization of materials over their lifecycle as well as model parameter estimation methodologies [Ref 6].

Material systems and related technologies of high interest include but are not limited to: • High temperature alloys • GRX-810 and other Ni-based oxide dispersion strengthened (ODS) alloys • Oxygen sensitive alloys (e.g. Nb-based) • Innovative Processing and Forming Techniques for High-Temperature Shape Memory Alloys • investigate ingot conversion processes and forming techniques through microstructure feature design and optimization, with the goal of overcoming challenges associated with processing these materials.

Studies on grain structure and microstructure as a function of cold or hot working, heat treatments, reduction ratios, and related parameters are required, using both experimental and computational approaches. Additionally, proposals should include investigations into ingot surface treatments designed to prevent potential cracking and material loss during processing.

• Utilizing Additive Manufacturing to Stabilize Shape Memory Alloy Responses • Nondestructive evaluation and characterization, including in-situ monitoring • Nondestructive evaluation and characterization • Nonlinear models and model applications at, and across, all relevant length scales • Environmental barrier coatings (EBC/CMC) • Multiphysics models capturing realistic microstructural features • Increased model accessibility through deployment of machine learning and artificial intelligence • Transport modeling of oxidizing species • AI agents for high-throughput materials simulation/discovery • Automated materials database curation • Data analysis/visualization tools for multiscale materials simulation • Novel multiscale and Multiphysics modeling approaches • Machine learning approaches for creating surrogates of high-fidelity simulations AI techniques to automate existing modeling workflows