Efficient Materials Informatics between Rockets and Electrons
Open Access
- Author:
- Krajewski, Adam
- Graduate Program:
- Materials Science and Engineering
- Degree:
- Doctor of Philosophy
- Document Type:
- Dissertation
- Date of Defense:
- May 20, 2024
- Committee Members:
- John Mauro, Program Head/Chair
Zi-Kui Liu, Chair, Minor Member & Dissertation Advisor
Allison Beese, Major Field Member
Wenrui Hao, Outside Unit & Field Member
Ismaila Dabo, Major Field Member - Keywords:
- materials informatics
materials discovery
scientific computing
machine learning
artificial intelligence
database design
high entropy alloys
compositionally complex materials
functionally graded materials
extreme environments
hypersonics
data driven
thermodynamics
graphs
compositional spaces
python
nim
CALPHAD
HEA
FGM
ULTERA
pySIPFENN
MPDD
alloy design
inverse design - Abstract:
- The true power of computational research typically can lay in either what it accomplishes or what it enables others to accomplish. In this work, both avenues are simultaneously embraced across several distinct efforts existing at three general scales of abstractions of what a material is - atomistic, physical, and design. At each, an efficient materials informatics infrastructure is being built from the ground up based on (1) the fundamental understanding of the underlying prior knowledge, including the data, (2) deployment routes that take advantage of it, and (3) pathways to extend it in an autonomous or semi-autonomous fashion, while heavily relying on artificial intelligence (AI) to guide well-established DFT-based ab initio and CALPHAD-based thermodynamic methods. The resulting multi-level discovery infrastructure is highly generalizable as it focuses on encoding problems to solve them easily rather than looking for an existing solution. To showcase it, this dissertation discusses the design of multi-alloy functionally graded materials (FGMs) incorporating ultra-high temperature refractory high entropy alloys (RHEAs) towards gas turbine and jet engine efficiency increase reducing CO2 emissions, as well as hypersonic vehicles. It leverages a new graph representation of underlying mathematical space using a newly developed algorithm based on combinatorics, not subject to many problems troubling the community. Underneath, property models and phase relations are learned from optimized samplings of the largest and highest quality dataset of HEA in the world, called ULTERA. At the atomistic level, a data ecosystem optimized for machine learning (ML) from over 4.5 million relaxed structures, called MPDD, is used to inform experimental observations and improve thermodynamic models by providing stability data enabled by a new efficient featurization framework.