Research Experiences for Undergraduates in Computational Methods with Applications in Materials Science
MSU Affiliation
James Worth Bagley College of Engineering; Department of Mechanical Engineering; Center for Computational Sciences
Research Mentor
Christopher Barrett
Creation Date
7-25-2025
Abstract
Technological progress is strongly coupled with our understanding of materials. In fields like aerospace, titanium-aluminum (Ti-Al) alloys are of particular interest for their strength-to-weight ratio and high-temperature performance. The ability to accurately predict these characteristics as a function of composition and processing conditions would reduce research costs by tuning the performance to the application's needs before producing the physical material. The mechanical properties, and performance by proxy, of a material are dependent on the microstructure of and the interatomic interactions within the material. Thus, we validate the rapid artificial neural network (RANN) potential of the titanium-aluminum binary system for the prediction of mechanical properties via tensile stress simulations in the LAMMPS software package. The RAN potential has predicted the simulation yield strength for a perfect crystal of pure titanium to be 15 GPa. We see that by randomly making about 5% of the hcp sites monovacancies, the yield strength was reduced to approximately half of the perfect crystal, while a spherical void with a radius of 5 lattice parameters had a similar but less pronounced effect. It was also found that by applying a grain boundary perpendicular to the basal plane of one grain and parallel to the basal plane of the other grain, the yield strength was reduced to about a fourth of that of the perfect crystal. By visualizing the simulations in OVITO, we observed the formation and disappearance of bcc layers as well as both deformation twinning and recrystallization twinning.
Presentation Date
Summer 7-31-2025
Keywords
binary alloys, metal defects
Recommended Citation
Reed, Carter and Barrett, Kip, "Neural Network Prediction of Titanium-Aluminum Mechanical Properties" (2025). Research Experiences for Undergraduates in Computational Methods with Applications in Materials Science. 14.
https://scholarsjunction.msstate.edu/ccs-reu/14
