For Focus Area RQ-05-1909, the Air Force Space Command seeks to develop technological capabilities in Multi-Mode Propulsion with Common Propellant using artificial intelligence (AI), machine learning (ML) and/or deep learning (DL) approaches to accelerate the discovery/design of new energetic in-space materials via accurate assessments of their physicochemical properties from available databases. In this project, Imagars will tailor its patent-pending ML technology (Patent Publication No. US 2020/0257933A1) to accelerate alloy design such as to enable acceleration in identification of new energetic materials (propellants with greater energy density than presently available). Enhancements to Imagarsâ existing database will involve properties related to thermal coefficients, thermal diffusability, energy density, phase transformations and entropy. For this purpose, Imagars will be targeting collaboration with Prof. Liney Arnadottir, from the Department of Chemical, Biological and Environmental Engineering at Oregon State University, a recognized expert in first-principle calculations. Data-driven approaches, which include intelligent predictive capabilities, provide opportunities to accelerate the discovery of such energetic materials. Continued application of test-driven approaches may not be cost-effective, sustainable, or sufficiently responsive to todayâs warfighter needs. To this end, sequential learning combines data-driven ML approaches with experimentation, for purpose of necessitating fewer experiments and decreasing cost. In addition, we present a framework for joint optimization of material properties. This framework was originally proposed for Ni-based super-alloys. But the fundamental ideas can be adapted both to high-entropy alloys (HEAs) and new energetic materials (propellant design). We intend on working with Prof. Arnadottir on fully defining the physio-chemical data sets suitable for the molecular engineering involved. In addition, we present a fallback scenario, which assumes application of the framework to joint optimization of mechanical properties of alloys, used for rocket propulsion, prior to application to energetic material (propellants), for the Air Force Space Command to consider. Our commercialization plan assumes collaboration with Lockheed Martin Space. Dr. Anand Kulkarni of Siemens is a co-author of our patent titled âAutomatic Requirement Verification Engine and Analyticsâ as well as patent publication 2020/0257933A1. For the patenting efforts, we intend to go through a similar process with Lockheed Martin Space as we have started with Siemens. Lockheed Martin is expected to provide requirements for design of energetic materials relevant to multi-mode propulsion, in the event of a Phase 2 award. In this case, Lockheed Martin is also expected to advise on integration of the proposed product, a plugin for joint optimization into established tools used for designing new energetic mat
