Date of Award
12-2025
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Materials Science and Engineering
Committee Chair/Advisor
Dr. Dilpuneet S. Aidhy
Committee Member
Dr. Enrique Martinez Saez
Committee Member
Dr. Garrett Pataky
Committee Member
Dr. Pejman Tahmasebi
Committee Member
Dr. Cheng Sun
Abstract
In the past decade, a paradigm shift in the design of metal alloys has been observed. These new alloys are commonly referred to as high entropy alloys (HEAs), multi-principal element alloys (MPEAs), or complex, concentrated alloys (CCAs). In contrast to conventional alloys, which consist of one main element (for example 80%) with other elements in small amounts, HEAs are made of four or more main elements ranging from 5 to 35% each element. Due to the large presence of multiple elements, HEAs have shown substantial material property improvements over conventional alloys such as steel. For example, they have high ductility and high strength, fracture toughness, resistance to irradiation damage, and creep resistance. Because of these properties and their applications in harsh environments, they are of great interest to the structural alloys community. However, due to the range of compositions available, it becomes extremely difficult to create each unique combination of elements for testing. Thus, computational studies are needed to reduce the time and effort spent searching for the alloy with desired properties. This is often done through theoretical calculations, which are typically at the atomic-length scale. But they can be computationally expensive and time consuming. To further reduce this computational cost, machine learning has made inroads in materials science. The work in this dissertation focuses on understanding the underlying mechanisms of HEAs and their properties based on theoretical calculations. Based on these insights, databases are constructed to train machine learning models for property predictions. These models accurately predict the strength of HEAs as well as their defect energies, significantly reducing the computational cost for efficient alloy design.
Recommended Citation
Linton, Nathan, "Integrating DFT and Machine Learning to Predict Structural Properties in High Entropy Alloys" (2025). All Dissertations. 4177.
https://open.clemson.edu/all_dissertations/4177
Author ORCID Identifier
0000-0003-1548-5613