Physical Data-Driven Characterization for Material Science Discovery & Design
Abstract
Testing and characterization are becoming more sophisticated and time-intensive as more (exotic and functional) materials are developed. From synthesis and manufacturing to characterization, monitoring, and optimization, machine learning has applications in materials science. The multi analysis tool (Bruker IconIR) will help us get a wealth of data that will serve two purposes Ð i) understand process-structure-property relationships in novel materials of interest to DOD, and in addition to novel machine learning/artificial intelligence (ML/AI) techniques, ii) explore the predictive performance of the materials. ML/AI coupled with first-principles models developed will significantly aid the additive manufacturing of these novel devices and composites of interest to DOD. However, advances must involve a materials genome-like approach to achieve novel discoveries in the 21st century. The goals and objectives of this area strongly align with newer priorities of the Air Force Office of Scientific Research from the newly released FA9550-21-S-0001, and across the Engineering of Complex Systems to the Information and Networks. Recently, scientists have combined multi-spectral techniques (hyperspectral, infrared, etc.) with atomic force microscopy. This greatly enhances measurement capabilities and helps perform chemical analysis and compositional mapping with spatial resolutions on the orders of nanometers (applications in bio-, electrical, and tribological studies). The rationale behind this proposed request is to bring together and develop faculty from across FAMU campus (Chemistry, Agriculture & Food Sciences, Environment, & Engineering disciplines) to increase research convergence of data-driven techniques and analytical characterization. We aim to study and investigate resultant process-structure relationships using the multi-analytical characterization tool to: i) study hybrid modeling via combinatorial digital twin models, ii) explore structural dynamics and rheology of preceramic polymer hairy nanoparticles, iii) advance EMI shielding of scalable manufactured structural nanocomposite, iv) utilize cyber-multi-material processing of hybrid-electronics, and broader projects in biological and chemistry. Material multifunctional performance benchmarks all depend on interfacial contacts composite constituents. There is a fundamental challenge relating interfacial mechanics to surface chemistry. This instrument will expose students to the AI/ML-driven material study and discovery, a critically important frontier for material research. Generating physical data for analysis has been constrained to analytical equipment that measures single properties; multi-analyses tools will extend the capabilities to understand fundamental interactions. Bruker user-based workshops will be hosted alongside FAMU Research Symposium to encourage its usage and educate a new user base as these capabilities do not currently exist. With a new research program in materials-by-design, the PI will leverage research training into a flagship multi-discipline course. The impact of the analytical characterization would drive the continuance of 1) rapidly promoting the advancement of new material discovery, 2) emphasize our commitment to providing access to cutting-edge research, and 3) enhance graduate research success both with emphasis on joint workforce needs at the DoD labs. The FAMU faculty is committed to ensuring this equipment is used to promote graduate students matriculation and graduation, which is vital to the national U.S. interest (WH Executive Order #13779). The proposed work will play a key role in the soon-to-be-established material science and engineering program for the first time at FAMU.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Aug 02, 2022
- Source ID
- W911NF2210148
Entities
People
- Rebekah Sweat
Organizations
- Army Contracting Command
- Florida A&M University
- Office of the Secretary of Defense