Materials Science & Engineering Research Works

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Photonic (computational) memories: tunable nanophotonics for data storage and computing
(De Gruyter, 2022-05-16) Lian, Chuanyu; Vagionas, Christos; Alexoudi, Theonitsa; Pleros, Nikos; Youngblood, Nathan; Ríos, Carlos
The exponential growth of information stored in data centers and computational power required for various data-intensive applications, such as deep learning and AI, call for new strategies to improve or move beyond the traditional von Neumann architecture. Recent achievements in information storage and computation in the optical domain, enabling energy-efficient, fast, and high-bandwidth data processing, show great potential for photonics to overcome the von Neumann bottleneck and reduce the energy wasted to Joule heating. Optically readable memories are fundamental in this process, and while light-based storage has traditionally (and commercially) employed free-space optics, recent developments in photonic integrated circuits (PICs) and optical nano-materials have opened the doors to new opportunities on-chip. Photonic memories have yet to rival their electronic digital counterparts in storage density; however, their inherent analog nature and ultrahigh bandwidth make them ideal for unconventional computing strategies. Here, we review emerging nanophotonic devices that possess memory capabilities by elaborating on their tunable mechanisms and evaluating them in terms of scalability and device performance. Moreover, we discuss the progress on large-scale architectures for photonic memory arrays and optical computing primarily based on memory performance.
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, Ichiro
Application 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.
Plasmonic nanoarcs: a versatile platform with tunable localized surface plasmon resonances in octave intervals
(Optical Society of America Publishing, 2020-10-12) Zhang, Kunyi; Lawson, Andrew P.; Ellis, Chase T.; Davis, Matthew S.; Murphy, Thomas E.; Bechtel, Hans A.; Tischler, Joseph G.; Rabin, Oded
The tunability of the longitudinal localized surface plasmon resonances (LSPRs) of metallic nanoarcs is demonstrated with key relationships identified between geometric parameters of the arcs and their resonances in the infrared. The wavelength of the LSPRs is tuned by the mid-arc length of the nanoarc. The ratio between the attenuation of the fundamental and second order LSPRs is governed by the nanoarc central angle. Beneficial for plasmonic enhancement of harmonic generation, these two resonances can be tuned independently to obtain octave intervals through the design of a non-uniform arc-width profile. Because the character of the fundamental LSPR mode in nanoarcs combines an electric and a magnetic dipole, plasmonic nanoarcs with tunable resonances can serve as versatile building blocks for chiroptical and nonlinear optical devices.
Printable, high-performance solid-state electrolyte films
(AAAS, 2020-11-18) Ping, Weiwei; Wang, Chengwei; Wang, Ruiliu; Dong, Qi; Lin, Zhiwei; Brozena, Alexandra H.; Dai, Jiaqi; Luo, Jian; Hu, Liangbing
Current ceramic solid-state electrolyte (SSE) films have low ionic conductivities (10−8 to 10−5 S/cm ), attributed to the amorphous structure or volatile Li loss. Herein, we report a solution-based printing process followed by rapid (~3 s) high-temperature (~1500°C) reactive sintering for the fabrication of high-performance ceramic SSE films. The SSEs exhibit a dense, uniform structure and a superior ionic conductivity of up to 1 mS/cm. Furthermore, the fabrication time from precursor to final product is typically ~5 min, 10 to 100 times faster than conventional SSE syntheses. This printing and rapid sintering process also allows the layer-by-layer fabrication of multilayer structures without cross-contamination. As a proof of concept, we demonstrate a printed solid-state battery with conformal interfaces and excellent cycling stability. Our technique can be readily extended to other thin-film SSEs, which open previously unexplores opportunities in developing safe, high-performance solid-state batteries and other thin-film devices.
Tuning the hysteresis of a metal-insulator transition via lattice compatibility
(Springer Nature, 2020-07-15) Liang, Y. G.; Lee, S.; Yu, H. S.; Zhang, H. R.; Liang, Y. J.; Zavalij, P. Y.; Chen, X.; James, R. D.; Bendersky, L. A.; Davydov, A. V.; Zhang, X. H.; Takeuchi, I.
Structural phase transitions serve as the basis for many functional applications including shape memory alloys (SMAs), switches based on metal-insulator transitions (MITs), etc. In such materials, lattice incompatibility between transformed and parent phases often results in a thermal hysteresis, which is intimately tied to degradation of reversibility of the transformation. The non-linear theory of martensite suggests that the hysteresis of a martensitic phase transformation is solely determined by the lattice constants, and the conditions proposed for geometrical compatibility have been successfully applied to minimizing the hysteresis in SMAs. Here, we apply the non-linear theory to a correlated oxide system (V1−xWxO2), and show that the hysteresis of the MIT in the system can be directly tuned by adjusting the lattice constants of the phases. The results underscore the profound influence structural compatibility has on intrinsic electronic properties, and indicate that the theory provides a universal guidance for optimizing phase transforming materials.

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