Optimizing Autonomous and Human-Assisted Experimentation in Materials Development
Abstract
Advances in machine intelligence have the potential to increase the capabilities of autonomous research systems and human-machine research systems. In this interdisciplinary proposal, we explore the application of these advances for accelerating materials development in both systems. The proposed 3-year project extends our on-going collaboration with Dr. Maruyama’s materials science team in the Air Force Research Laboratory, and focuses on three complementary lines of closed-loop research: (1) Multi-objective optimization algorithms will be developed to automate the synthesis of carbon nanotubes that are customized to given specifications; (2) Human-guided Bayesian optimization algorithms will be explored in additive manufacturing (3D printing) where evaluation of product quality by human experts may be superior to sensor measurements; (3) Automated machine learning approaches will be applied to the autonomous research systems in (1) and (2) to further improve performance while saving time and effort for fine-tuning the systems. Results of this work should also advance discovery in other areas of materials science.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jan 21, 2022
- Source ID
- FA95502110176XX0
Entities
People
- Mark Pitt
Organizations
- Air Force Office of Scientific Research
- Ohio State University
- United States Air Force