Tyler J. Harrington | Searcharxiv

arXiv2020

Dual-Phase High-Entropy Ultra-High Temperature Ceramics

A series of dual-phase high-entropy ultrahigh temperature ceramics (DPHE-UHTCs) are fabricated starting from N binary borides and (5-N) binary carbides powders. >~99% relative densities have been achieved with virtually no native oxides. These DPHE-UHTCs consist of a hexagonal high-entropy boride (HEB) phase and a cubic high-entropy carbide (HEC) phase. A thermodynamic relation that governs the compositions of the HEB and HEC phases in equilibrium is discovered and a thermodynamic model is proposed. These DPHE-UHTCs exhibit tunable grain size, Vickers microhardness, Young' and shear moduli, and thermal conductivity. The DPHE-UHTCs have higher hardness than the weighted linear average of the two single-phase HEB and HEC, which are already harder than the rule-of-mixture averages of individual binary borides and carbides. This study extends the state of the art by introducing dual-phase high-entropy ceramics (DPHECs), which provide a new platform to tailor various properties via changing the phase fraction and microstructure.

cond-mat.mtrl-sci↗

arXiv2019

Paradigm shift in electron-based crystallography via machine learning

Accurately determining the crystallographic structure of a material, organic or inorganic, is a critical primary step in material development and analysis. The most common practices involve analysis of diffraction patterns produced in laboratory XRD, TEM, and synchrotron X-ray sources. However, these techniques are slow, require careful sample preparation, can be difficult to access, and are prone to human error during analysis. This paper presents a newly developed methodology that represents a paradigm change in electron diffraction-based structure analysis techniques, with the potential to revolutionize multiple crystallography-related fields. A machine learning-based approach for rapid and autonomous identification of the crystal structure of metals and alloys, ceramics, and geological specimens, without any prior knowledge of the sample, is presented and demonstrated utilizing the electron backscatter diffraction (EBSD) technique. Electron backscatter diffraction patterns are collected from materials with well-known crystal structures, then a deep neural network model is constructed for classification to a specific Bravais lattice or point group. The applicability of this approach is evaluated on diffraction patterns from samples unknown to the computer without any human input or data filtering. This is in comparison to traditional Hough transform EBSD, which requires that you have already determined the phases present in your sample. The internal operations of the neural network are elucidated through visualizing the symmetry features learned by the convolutional neural network. It is determined that the model looks for the same features a crystallographer would use, even though it is not explicitly programmed to do so. This study opens the door to fully automated, high-throughput determination of crystal structures via several electron-based diffraction techniques.

cond-mat.mtrl-sci↗