Materials Science & Engineering Research Works
Permanent URI for this collectionhttp://hdl.handle.net/1903/1660
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Item Spatial and temporal control of glassy-crystalline domains in optical phase change materials(Wiley, 2023-11-07) Lee, Chih-Yu; Lian, Chuanyu; Sun, Hongyi; Huang, Yi-Siou; Acharjee, Niloy; Takeuchi, Ichiro; Rios Ocampo, Carlos A.Chalcogenide phase change materials (PCMs) have become one of the most promising material platforms for the Optics and Photonics community. The unparalleled combination of nonvolatility and large optical property modulation promises devices with low-energy consumption and ultra-compact form factors. At the core of all these applications lies the difficult task of precisely controlling the glassy amorphous and crystalline domains that compose the PCM microstructure and dictate the optical response. A spatially controllable glassy-crystalline domain distribution is desired for intermediate optical response (vs. binary response between fully amorphous and crystalline states), and temporally resolved domains are sought after for repeatable reconfiguration. In this perspective, we briefly review the fundamentals of PCM phase transition in various reconfiguring approaches for optical devices. We discuss each method's underpinning mechanisms, design, advantages, and downsides. Finally, we lay out current challenges and future directions in this field.Item A semi-supervised deep-learning approach for automatic crystal structure classification(Wiley, 2022) Lolla, Satvik; Liang, Haotang; Kusne, A. Gilad; Takeuchi, Ichiro; Ratcliff, WilliamThe structural solution problem can be a daunting and time-consuming task. Especially in the presence of impurity phases, current methods, such as indexing, become more unstable. In this work, the novel approach of semi-supervised learning is applied towards the problem of identifying the Bravais lattice and the space group of inorganic crystals. The reported semi-supervised generative deep-learning model can train on both labeled data, i.e. diffraction patterns with the associated crystal structure, and unlabeled data, i.e. diffraction patterns that lack this information. This approach allows the models to take advantage of the troves of unlabeled data that current supervised learning approaches cannot, which should result in models that can more accurately generalize to real data. In this work, powder diffraction patterns are classified into all 14 Bravais lattices and 144 space groups (the number is limited due to sparse coverage in crystal structure databases), which covers more crystal classes than other studies. The reported models also outperform current deep-learning approaches for both space group and Bravais lattice classification using fewer training data.Item Tuning the temperature range of superelastic Ni-Ti alloys for elastocaloric cooling via thermal processing(Institute of Physics, 2023-04-28) Yamazaki, Takahiro; Montagnoli, Andre L.; Young, Marcus L.; Takeuchi, IchiroCaloric cooling enlisting solid-state refrigerants is potentially a promising eco-friendly alternative to conventional cooling based on vapor compression. The most common refrigerant materials for elastocaloric cooling to date are Ni-Ti based superelastic shape memory alloys. Here, we have explored tuning the operation temperature range of Ni50.8Ti49.2 for elastocaloric cooling. In particular, we have studied the effect of thermal treatments (a.k.a. aging) on the transformation temperature, superelasticity, and elastocaloric effects of Ni50.8Ti49.2 shape memory alloy tubes. The isothermal compressive test revealed that the residual strain of thermally-treated Ni-Ti tubes at room temperature approaches zero as aging time is increased. Short-time aging treatment at 400 ◦C resulted in good superelasticity and elastocaloric cooling performance with a large tunable austenite finish (Af) temperature range of 24.7 ◦C, as determined from the Af temperature of the samples that were aged 5–120 min. The main reason of the property change is the formation of a different amount of Ni4Ti3 precipitates in the NiTi matrix. Our findings show that it is possible to tailor the Af temperature range for development of cascade elastocaloric cooling systems by thermally treating a starting single composition Ni-Ti alloy.Item Physics in the Machine: Integrating Physical Knowledge in Autonomous Phase-Mapping(Frontiers, 2022-02-16) Kusne, A. Gilad; McDannald, Austin; DeCost, Brian; Oses, Corey; Toher, Cormac; Curtarolo, Stefano; Mehta, Apurva; Takeuchi, IchiroApplication of artificial intelligence (AI), and more specifically machine learning, to the physical sciences has expanded significantly over the past decades. In particular, science-informed AI, also known as scientific AI or inductive bias AI, has grown from a focus on data analysis to now controlling experiment design, simulation, execution and analysis in closed-loop autonomous systems. The CAMEO (closed-loop autonomous materials exploration and optimization) algorithm employs scientific AI to address two tasks: learning a material system’s composition-structure relationship and identifying materials compositions with optimal functional properties. By integrating these, accelerated materials screening across compositional phase diagrams was demonstrated, resulting in the discovery of a best-in-class phase change memory material. Key to this success is the ability to guide subsequent measurements to maximize knowledge of the composition-structure relationship, or phase map. In this work we investigate the benefits of incorporating varying levels of prior physical knowledge into CAMEO’s autonomous phase-mapping. This includes the use of ab-initio phase boundary data from the AFLOW repositories, which has been shown to optimize CAMEO’s search when used as a prior.