Abstract
The multicomponent Ti alloys, specifically the β-phase, have experienced a strong growth over the last decades, due to their outstanding properties of ultra-high strength and low Young's modulus. These properties play a significant role in many aerospace and biomedical applications. Selection and optimization of multicomponent alloys is challenging due to the vast chemical and compositional space. Here we investigate the use of machine learning techniques informed by density functional calculations to guide the selection of Nb- and Zr-based Ti binary alloys. From the cubic structures obtained from high throughput calculations and literature, we identify several structures with Young's moduli below 40 GPa. The multivariant decision tree methods provide efficient surrogate models to identify structure variables have high influences on the energetic stability and Young's modulus. We implement a workflow of incorporating DFT provided results and machine learning method to explore the chemical and composition space of other binary and multicomponent alloys, to eventually accelerate the material design via taking advantages of identified key variables.
| Original language | English |
|---|---|
| Article number | 109830 |
| Journal | Computational Materials Science |
| Volume | 184 |
| DOIs | |
| State | Published - Nov 2020 |
Funding
W.S. acknowledges the National Natural Science Foundation of China (Grant No. 51902052 ). P.K. and (partially) W.S. were supported as part of the Fluid Interface Reactions, Structures and Transport (FIRST) Center, an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences. I.D.M. was supported by appointments to the JRG program at the APCTP through the Science and Technology Promotion Fund and Lottery Fund of the Korean Government, the Korean Local Governments-Gyeongsangbuk-do Proving and Pohang City. O.I.G. acknowledges support provided by the Ministry of Education and Science of the Russian Federation (No. 074-02-2018-329 from May 16, 2018). This research used computational resources of Swedish National Infrastructure for Computing ( SNIC ) under the project of SNIC2019/3-580 as well as the National Energy Research Scientific Computing Center ( NERSC ), a U.S. Department of Energy Office of Science User Facility operated under Contract No. DE-AC02-05CH11231. We also thank the Big Data Computing Center of Southeast University for providing the facility support on the numerical calculations in this paper.
Keywords
- Combinatorial materials science
- Density functional theory
- Energetic stability and Young's modulus
- High-throughput and data mining