The research emphasis of the Consortium for Nuclear Security Advanced Manufacturing Enhanced by Machine Learning (NSAM-ML) is on advanced manufacturing (AM), the discovery and development of new high-strength materials for special nuclear applications, and nanomaterials and nano-sensors for monitoring the health of the national nuclear infrastructure. Data-driven discovery leverages the strong materials science expertise of the various Consortium teams. The five research thrusts at the core of this project are as follows:
- Predictive modeling, fabrication and characterization of advanced materials for (a) new types of sensors to monitor health of nuclear infrastructure at the micron and submicron scales, as well as other special devices, (b) superalloys, high-entropy alloys, and high-temperature cladding for plasma reactors, and (c) new nanocomposites of superior functionalities
- Device physics (nanosensors, quantum devices, etc) supported by high-performance computing
- Developing new AM paradigms and tailoring the material processing to suit AM concepts and to make use of the material nanoscale properties
- New pathways for solar energy harvesting and generation of alternative fuel from CO2
- Machine learning for improving materials properties and the manufacturing of nanomaterials and nano-devices.
Materials of interest include piezoelectric and ferroelectric nanomaterials for a variety of applications (e.g., sensing microscopic strains and cracks for monitoring the health of critical infrastructure); modified silicon to increase infrared radiation (IR) absorption via the intermediate band (IB); IR photosensors and other detectors that use IB semiconductors; structural and functional nanocomposites for developing outstanding physical properties; super-hard and high-temperature refractory alloys; plasmonic materials; highly efficient silicon solar cells with added efficiency in the IR (e.g., IB-based silicon), perovskite materials on silicon that significantly increase the efficiency of silicon solar cells; and surface nanocluster catalysts for the generation of alternative fuel from CO2.
This research not only serves the goals of this particular project but also has ramifications in many other fields, including but not limited to IR sensors, solar materials and technologies, silicon nano-optoelectronics, and optical integration in integrated circuit technologies for next-generation computers and smart devices, such as a future smart IR camera and smart electronic nose, to name a few.
Data produced using the most advanced state-of-the-art analytical tools in the National Labs will feed machine-learning models, which will accelerate the scientific endeavor in the mentioned specific areas. For instance, using regimented carbon nanotubes (CNT) and graphene will be sought to produce significantly enhanced physical properties that can be used to make many new functionalities (for instance, artificial piezoelectricity in thin films). Strong piezoelectricity in thin films is desirable for designing the most sensitive sensor capable of measuring sub-micron scale strain in three directions. Such an ambitious goal will support the safety of the National Nuclear Security Administration (NNSA) infrastructure cost-effectively.
Additionally, infrastructure health is a widespread issue in energy production machinery, e.g., nuclear plants, and aerospace equipment. The Elizabeth City State University (ECSU) partner produces catalyst nanoclusters with the Consortium colleagues, a promising route for the generation of fuel from dead carbon dioxide. While this solves the fuel sustainability issue, it also promises a solution for the largest pollution due to gas emissions. Also, our Southern University and A&M College (SUBR) consortium member cooperates in producing high-temperature superalloys, which will also support NNSA's goals of ensuring the safety of nuclear infrastructure.