The overarching goal of the High-Throughput Materials Discovery for Extreme Conditions (HTMDEC) program is to couple automation and machine learning (ML) techniques to material manufacturing and characterization to demonstrate new materials that withstand and perform under extreme conditions. The program will develop the necessary methodologies, models, algorithms, synthesis and processing techniques, and requisite characterization and testing to rapidly accelerate the discovery of novel materials through data-driven approaches. As such, it is expected the results of this program will be the above techniques as well as novel materials exhibiting unprecedented properties at the appropriate scales that have been developed utilizing all of the aforementioned tools which will be provided to DEVCOM-ARL for further analysis and testing (HTMDEC FOA 2021).
HTMDEC is composed of four general thrust areas: data-driven material design, high-throughput synthesis and processing, high-throughput characterization, and ML-augmented physics-based models. Additionally, there are two targeted thrust areas: center development, and data handling and management.
In 2022, the first year of this program, the DEVCOM Army Research Laboratory awarded seed grants for the thrust areas. Johns Hopkins University (JHU) faculty are involved in four of the seed grants.
- New Approaches to High-Throughput Characterization of Materials for Extreme Conditions; JHU PIs: Prof. KT Ramesh and Prof. Todd Hufnagel; Co-PI: Prof. Dan Gianola (UCSB).
- Data at the Speed of Extreme Materials Discovery: A Seedling Proposal for HTMDEC Data Handling and Management; JHU PIs: Mr. Dave Elbert and Dr. Gerard Lemson; Co-PI: Prof. Matt Turk (U of Illinois).
- Development and Deployment of a Bayesian Framework for the Accelerated Machine Learning of Multiscale Physics Controlling Material Responses in Extreme Environments; JHU Co-PI: Prof. Lori Graham-Brady.
- Multimodal Data-Driven Design of Materials for Extreme Environments; JHU Co-PIs: Prof. Lori Graham-Brady and Prof. Michael Shields.
Research was sponsored by the Army Research Laboratory and was accomplished under Cooperative Agreement Number W911NF-22-2-0121/0101. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein.