CITATION.cff

cff-version: 1.2.0
title: >-
  Efficient Materials Informatics between Rockets and
  Electrons
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Adam M
    family-names: Krajewski
    email: adam@phaseslab.org
    affiliation: The Pennsylvania State University
    orcid: 'https://orcid.org/0000-0002-2266-0099'
identifiers:
  - type: url
    value: 'https://arxiv.org/abs/2407.04648'
    description: arXiv
repository-code: 'https://github.com/amkrajewski/PhD-Dissertation'
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.
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
license: CC-BY-NC-SA-4.0