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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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Abstract: Super-hard materials exhibit a Vickers hardness H ≥ 40 GPa, and they have extensive industrial and technological applications. Due to the huge search space of possible element combinations, it is challenging to explore new superhard ternary or quaternary materials. In this talk, I will first discuss machine learning (ML) discovery of new superhard B-C-N and B-N-O compounds. The ML results are validated by evolutionary structure prediction and density functional theory (DFT). In particular, the proposed BC10N has a low formation energy and a high hardness H ~ 86 GPa only next to diamond. In the second part of the talk, I will discuss calculations of entropy formation ability (EFA) using both DFT and ML for 5-metal hexagonal high-entropy borides. The calculations indicate that EFA serves as a good descriptor for the synthesizability of high-entropy materials, some of which have superior mechanical properties promising for applications in extreme environments.
Bio: Cheng-Chien Chen received his Ph.D. in Physics from Stanford University in, 2011. Afterwards he became a Postdoctoral Scholar at the SLAC National Accelerator Laboratory and later an Aneesur Rahman Postdoctoral Fellow at the Argonne National Laboratory. He joined the Department of Physics at the University of Alabama at Birmingham (UAB) as an Assistant Professor in 2016. He is currently a Leadership Resource Allocation (LRAC) Awardee to use the NSF-funded Frontera Supercomputer and an NSF EPSCoR Research Fellow. Dr. Chen’s expertise lies in using quantum many-body simulations, first-principles calculations, and machine learning approaches to model and predict the properties of various quantum and functional materials.
