Machine learning assisted design of biomedical high entropy alloys with low elastic modulus for orthopedic applications

High entropy alloys (HEAs) have received considerable attention from the scientific community since the 2000s due to their excellent properties and potential to be used in various structural and functional applications. HEAs consist of multi-principal elements governing their final properties in addition to manufacturing methods and heat-treatment processes compared to traditional alloys, whose properties are governed by one main principal element. Therefore, understanding the effect of each element on the properties of HEAs is a complex process. Due to the "cocktail effect", one of the core four effects of HEAs, HEAs can attain unpredictable and outstanding performance superior to the performance of all constituent elements. Because HEAs consist of multiple elements, thousands of possible compositions can be developed, giving rise to different properties for the same family of HEAs. Hence, conventional trial-and-error methods become costly and inefficient in discovering new HEAs. A solution to this issue is using computational methods, such as density functional theory (DFT), molecular dynamics (MD), or machine learning (ML). However, DFT and MD are computationally expensive and time- consuming. On the contrary, ML is an efficient tool for establishing complex and non- linear relations between inputs and target property, making the new HEA discovery process faster and cheaper. In this thesis, three new biomedical HEAs, namely, Hf27Nb12Ta10Ti23Zr28, Hf30Nb14Ta10Ti28Zr18, and Hf12Nb16Ta35Ti29Zr8 were designed and developed utilizing ML. In the first chapter, Hf27Nb12Ta10Ti23Zr28 and Hf30Nb14Ta10Ti28Zr18 HEAs with low elastic modulus, closer to that of the bone, were predicted to reduce the "stress shielding" effect between the bone and implant material. Predictions were validated through experimental methods. In the second chapter, in order to enhance the antibacterial properties of HEAs developed in the previous chapter, they were coated with Ag via Physical Vapor Deposition (PVD). Specifically, the effect of PVD process parameters on Ag coatings' mechanical and ion release behavior was investigated. In the following chapter, the microstructure, surface oxide layer properties, and corrosion behavior of Hf27Nb12Ta10Ti23Zr28 and Hf30Nb14Ta10Ti28Zr18 HEAs were studied, revealing that they exposed superior corrosion behavior in simulated body fluid (SBF) and artificial saliva (AS) compared to conventional implant material, CoCrMo. Lastly, a new corrosion-resistant biomedical Hf12Nb16Ta35Ti29Zr8 HEA was developed utilizing ML in the fourth chapter. It was found that the produced ingot had a dendritic microstructure in the center and a homogeneous microstructure around the circumference. Samples cut from the homogenous part of the ingot showed outstanding corrosion resistance as opposed to the samples with dendritic microstructure and conventional implant material, CoCrMo. Overall, the findings of the thesis prove that ML methods can be utilized to discover new HEAs with desired properties.