Machine learning for shock compression of solids using scarce data
Abstract
Data-driven machine learning techniques can be useful for the rapid evaluation of material properties in extreme environments, particularly in cases where direct access to the materials is not possible. Such problems occur in high-throughput material screening and material design approaches where many candidates may not be amenable to direct experimental examination. In this paper, we perform an exhaustive examination of the applicability of machine learning for the estimation of isothermal shock compression properties, specifically the shock Hugoniot, for diverse material systems. A comprehensive analysis is conducted where effects of scarce data, variances in source data, feature choices, and model choices are systematically explored. New modeling strategies are introduced based on feature engineering, including a feature augmentation approach, to mitigate the effects of scarce data. The findings show significant promise of machine learning techniques for design and discovery of materials suited for shock compression applications.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Apr 21, 2023
- Source ID
- 10.1063/5.0146296
Entities
People
- Brian C. Barnes
- Francis G. VanGessel
- Mark Fuge
- Peter W Chung
- Ruth M. Doherty
- Sangeeth Balakrishnan
- William H. Wilson
- Zois Boukouvalas
Organizations
- American University
- Energetics Technology Center
- Naval Surface Warfare Center
- Office of Naval Research
- United States Army Research Laboratory
- University of Maryland