Materials Genome, CALPHAD, and a Career over the Span of 20, 50, and 60 Years: An FMD/SMD Symposium in Honor of Zi-Kui Liu: Poster Session
Sponsored by: TMS Functional Materials Division, TMS Structural Materials Division, TMS Materials Processing and Manufacturing Division, TMS: Alloy Phases Committee, TMS: Integrated Computational Materials Engineering Committee
Program Organizers: Yu Zhong, Worcester Polytechnic Institute; Richard Otis, Jet Propulsion Laboratory; Bi-Cheng Zhou, University of Virginia; Chelsey Hargather, New Mexico Institute of Mining and Technology; James Saal, Citrine Informatics; Carelyn Campbell, National Institute of Standards and Technology
Tuesday 5:30 PM
March 21, 2023
Room: Exhibit Hall G
Location: SDCC
M-23: Electronic Origin of Phase Stability in Mg–Zn–Y Alloys with a Long-Period Stacking Order: A First-Principles Study: Takao Tsumuraya1; Hiroyoshi Momida2; Tamio Oguchi2; 1Kumamoto University; 2Osaka University
A dilute Mg-1Zn-2Y (at%) alloy exhibits a tensile yield strength of ∼600 MPa [1]. This strength is coupled with the appearance of a unique atomistic structure called long-period stacking order (LPSO). This study examines the origin of the phase stability of the 18R LPSO structure using first-principles density-functional theory calculations [2]. The heat of formation as a function of the number of Zn vacancies is calculated to evaluate the role of Zn atoms. The calculated convex hull indicates that the Zn atoms in LPSO are stable even at about half of the Y atoms. The partial density of states of Mg atoms nearest to the Zn atoms forms a valley structure due to the bonding state with Zn atoms, leading to the stability of the LPSO structure. [1] Y. Kawamura et al, Mater. Trans. 42 (2001) 1172. [2] T. Tsumuraya et al, Appl. Phys. Express, 15 (2022) 075506 (arXiv 2206.10873).
M-24: Revealing the Materials Genome for Advanced High-entropy Materials: Jiaqi Lu1; William Yi Wang1; Fengpei Zhang1; Pingxiang Zhang1; Jinshan Li1; 1Northwestern Polytechnical University
The fundamental understandings of the atomic and electronic principles are important to reveal the origins for the excellent mechanical and physical properties of High entropy Materials (HEMs). Herein, we propose a machine learning and theoretical knowledge-based design system of super-hard high-entropy boride ceramics (HEBs) to designing HEBs more efficient and target in vast composition space. This evaluation model was obtained by screened and trained from 149 characteristics and 9 algorithms, which can screen out intrinsic features closely related to performance mechanism. Thus, realized the performance evaluation and screening design of multivariate, multiphase and multiparameter coupling complex systems quickly. Moreover, the Shapley additive explanation the key influence trend for material hardness with the change of HEBs electronic properties. Combined with first-principles calculation, the material component-property influence mechanism can be analysis effectively from the atomic and electronic bottom layer. This strategy can reveal the target performance design for HEMs efficiently from knowledge.