Kyle Daniel Miller

Keywords

Materials design and discovery  |  Sequential/active learning  |  Explainable AI  |  Physics-informed models  |  Uncertainty quantification  |  Structure-property relationships  |  Density functional theory (DFT)  |  Ferroelectric  |  Metal-insulator transition

Summary

• I create data-driven solutions for materials problems.
• I excel in collaborative, inquisitive environments.
• I prioritize efficient communication and fast failure.
• I value mutual respect and inclusive practices.

Education

• PhD. in Materials Science and Engineering @ Northwestern University
• B.S. in Physics, Mathematics @ University of Puget Sound
• Summer REU in perovskite synthesis @ University of Minnesota
• Summer REU in molecular dynamics @ Boise State University

Current Work

Research Scientist @ Citrine Informatics, External Research Department
⇒ Designing AI/ML-driven development workflows for nuclear waste forms, catalysts, magnets, superconductors, and printed alloys
⇒ Managing ongoing projects & writing proposals for multi-institution interdisciplinary government grants
⇒ Presenting at conferences to disseminate research and build collaborator network
⇒ Advising customer teams on how to best leverage the Citrine Platform for their particular design challenges

 

Recent Work

Graduate Research Intern @ Sandia National Laboratories
⇒ Building neural network surrogate models for electron-level simulation
⇒ Implementing active learning and data preprocessing techniques for the Materials Learning Algorithms (MALA) framework
 

NSF Graduate Research Fellow @ Northwestern University, MTD Group
⇒ Developing decoratypes, an extensible crystal structure taxonomy for site-based properties ⇒ Creating DFT+ML workflows for discovering and tuning ferroelectric materials
⇒ Using first-principles calculation to design metal-insulator transition materials