arXiv subjects

Chih-Yu Lee

Publications and source records attributed to Chih-Yu Lee.

10 recordsLinked to original sources

arXiv2026

Synthesis of Bulk Superconducting LiNbO$_2$ Crystals through CaH$_2$ Reduction

We have synthesized layered superconducting LiNbO$_2$ crystals through a bulk phase transformation from LiNbO$_3$ single crystals via CaH$_2$ reduction. As the Nb valence is reduced from 5+ to 3+, the material undergoes a structural transformation to the resulting product, LiNbO$_2$, which is accompanied by metallic behavior and a superconducting transition, Tc onset, as high as 14.4 K. Secondary ion mass spectroscopy (SIMS) and X-ray photoelectron spectroscopy (XPS) show that the resulting phase is hole-doped through de-lithiation during the reduction. Magnetization and AC susceptibility measurements from a tunnel diode resonator confirm the bulk nature of superconductivity with a superconducting volume fraction of approximately 77% and an upper critical field approaching 26 T. Our study demonstrates extreme hydride reduction as an effective method to induce phase transformations with non-topotactic pathways and can be used to synthesize bulk materials with exotic properties.

cond-mat.supr-con↗

arXiv2026

Real-time Multi-instrument Autonomous Discovery of Novel Phase-change Memory Materials

Autonomous labs enable the integration of automated experiment execution, data analysis and decision making. The main challenge remains the integration of diverse data streams from multiple instruments, where the data is often heterogeneous and unsynchronized. The standard learning process of undetermined synthesis-process-structure-property relationships (SPSPR) usually relies on post-experiment analysis after data is fully collected, not during live experiments, and decision making is carried out independently across characterization equipment. Here, we demonstrate the Multi-instrument Autonomous Discovery (MAD) framework -- combining structural property mapping and functional property optimization simultaneously in a closed-loop manner. As an example, we applied MAD to phase change memory (PCM) materials, and, in particular on the Mn-Sb-Te ternary, a previously unexplored materials system for PCM. A multi-output model is employed to merge data from x-ray diffraction (XRD) and electrical resistance measurements simultaneously through a co-regionalization kernel that models the relationship between them. The output probabilistic posterior and uncertainty quantification facilitate decision making with shared knowledge, while the goals are different across tasks. We aimed to maximize the knowledge of crystal structure distribution using non-negative matrix factorization (NMF), while in parallel, we find the composition with the maximum resistance value, an important figure of merit for PCM. Leveraging MAD, we found promising electrical PCMs and identified the SPSPR within 25 closed-loop iterations, corresponding to a seven-fold speed-up. The framework opens a new path of study in large-scale autonomous facilities, where future experiments can be run in parallel together, not independently.

cond-mat.mtrl-sci↗

arXiv2026

Combinatorial Survey of Structural Phase Distribution and Magnetism in Fe-Ge-Te Composition-spread Thin Film Libraries

Recently, magnetic 2-dimensional (2D) van der Waals (vdW) materials have garnered tremendous attention. The vdW ferromagnet Fe5Ge1Te2 has a Curie temperature Tc of ~ 270 K, which is tailorable by tuning the stoichiometry and the Fe deficiency to reach room temperature. To explore the expanded compositional space, we implemented combinatorial synthesis and high-throughput characterization to investigate the structural phase distribution and ferromagnetism of a Fe-Ge-Te thin film library. The library was prepared by magnetron co-sputtering followed by annealing in vacuum or in an inert environment. Composition and structural phase distribution of the 177 pads in the library were characterized using high-throughput wavelength dispersive spectroscopy (WDS), X-ray diffraction (XRD), and two-point probe resistance measurements. We leverage unsupervised machine learning to cluster the XRD dataset into groups of compositions with similar structural phases, and further study the ferromagnetic properties via SQUID magnetometry and X-ray magnetic circular dichroism (XMCD) across different clusters. The results are compared against magnetization and structural models calculated using DFT. Our results demonstrate that the hexagonal crystal structure is a critical prerequisite for ferromagnetism in this system, and that unexplored materials adopting this structure can be efficiently identified as possible ferromagnetic materials using our high-throughput, ML-assisted framework. This workflow based on the combinatorial strategy allows us to rapidly capture the composition-structure-magnetic property map across a broad compositional landscape of novel magnetic materials.

cond-mat.mtrl-sci↗

arXiv2026

Autonomous epitaxial atomic-layer synthesis via real-time computer vision of electron diffraction

Autonomous science platforms which make decisions on the fly are fundamentally changing the outlook for materials development. AI-driven schemes can effectively reduce the total number of iterations needed to arrive at the best stoichiometry for desired properties or optimum synthesis parameters by significant margins. Here, we demonstrate real-time closed-loop autonomous navigation of a multi-dimensional synthesis parameter space for fabricating phase-pure epitaxial films of a metastable functional oxide phase using pulsed laser deposition. Sequential growth iterations in search of the optimized recipe to stabilize the desired crystal phase were performed using frame-by-frame quantitative computer vision of electron diffraction images at the unit-cell level. Our scheme regularly resulted in > 30-fold reduction in the number of experiments compared to comprehensive parameter-space mapping. The real-time workflow developed here can be readily extended to other thin film synthesis platforms opening the door for self-driving atomic-level materials design as well as autonomous semiconductor manufacturing.

cond-mat.mtrl-sci↗

arXiv2025

Segmentation-Engineered Ge4Sb6Te7 Switch on SOI Platform for Multilevel Non-Volatile Photonic Neural Inference

Phase-change materials (PCMs) have emerged as key enablers of non-volatile, ultra-compact photonic switches for energy-efficient deep neural network (DNN) applications. In this work, we investigate the recently discovered $\mathrm{Ge_{4}Sb_{6}Te_{7}}$ (GST-467) as a high-contrast optical PCM and demonstrate its suitability for multi-level photonic computing. The complex refractive indices of amorphous and crystalline GST-467 were experimentally extracted and used to propose a segmented silicon-on-insulator photonic switch optimized at 1550 nm. Three-dimensional FDTD simulations reveal that segmentation significantly enhances the extinction ratio while maintaining low insertion loss, resulting in a more than seven times higher design figure of merit than an unsegmented design. Laser-induced thermo-optical simulations further establish efficient, reversible switching with sub-nJ energy requirements for crystallization and amorphization. Compared with established GST, GSST, and GSS compositions, GST-467 provides the largest transmission contrast and supports up to 48 resolvable optical states. When deployed as multi-level weights in photonic DNN architectures, the GST-467 switch achieves superior classification accuracy on EMNIST and Fashion-MNIST benchmarks. These results position GST-467 as a highly promising PCM for scalable, low-energy photonic computing and neuromorphic hardware.

physics.optics↗

arXiv2025

Incorporating Si into Sb2Se3: Tailoring Optical Phase Change Materials via Nanocomposites

Chalcogenide-based optical phase change materials (OPCMs) exhibit a large contrast in refractive index when reversibly switched between their stable amorphous and crystalline states. OPCMs have rapidly gained attention due to their versatility as nonvolatile amplitude or phase modulators in various photonic devices. However, open challenges remain, such as achieving reliable response and transparency spanning into the visible spectrum, a combination of properties in which current broadband OPCMs (e.g., Ge2Sb2Se4Te1, Sb2Se3, or Sb2S3) fall short. Discovering novel materials or engineering existing ones is, therefore, crucial in extending the application scope of OPCMs. Here, we use magnetron co-sputtering to study the effects of Si doping into Sb2Se3. We employ ellipsometry, X-ray diffraction, Raman spectroscopy, and scanning and transmission electron microscopy to investigate the effects of Si doping on the optical properties and crystal structure and compare these results with those from first principles calculations. Moreover, we study the crystallization and melt-quenching of thin films via nano-differential scanning calorimetry (NanoDSC). Our experiments demonstrate that 20% Si doping increases the transparency window in both states, specifically to 800 nm (1.55 eV) in the amorphous phase, while reducing power consumption by lowering the melting temperature. However, this reduction comes at the cost of reducing the refractive index contrast between states and slowing the kinetics of the phase transition.

cond-mat.mtrl-sci↗

arXiv2025

Persistent Fluctuating Superconductivity and Planckian Dissipation in Fe(Te,Se)

Increasingly intricate phase diagrams in new classes of superconductors host fascinating interactions between superconductivity, diverse quantum phases, and quantum critical dynamics. The native superfluids, however, often exhibit much lower density and much greater inhomogeneity than conventional superfluids. This may render the superconductivity susceptible to fluctuations that are ordinarily assumed to be frozen out far below the superconducting transition temperature $T_c$, calling into question the degree to which the superconducting state is fully coherent. In this work, we leverage terahertz spectroscopy to demonstrate strongly fluctuating superconductivity in topological compositions of the multiband iron-based superconductor Fe(Te,Se). These fluctuations are found to persist undiminished far below $T_c$ and converge upon the limit of Planckian dissipation above $T_c$. These results indicate that extended quantum fluctuations dominate the electrodynamics of both the superconducting and Planckian-dissipative precursor states of Fe(Te,Se), and demonstrate that the assumption of phase coherence must be rigorously validated in emerging classes of unconventional superconductors.

cond-mat.supr-con↗

arXiv2025

In Vivo Study of Bone Growth Around Additively Manufactured Implants with Ti-6Al-4V and Bioactive Glass Powder Composites

Osseointegration is crucial to the success of biomedical implants. Additive manufacturing of implants offers a high degree of design freedom, enabling precise control over implant geometry and material composition. Bioactive glass (BG) can substantially enhance bone binding and bioactivity; however, limited research has been conducted on its incorporation into additively manufactured implants. The performance of BG varies depending on the incorporation method, and the spatial and temporal evolution of its integration remains unclear. In this study, we synthesized Ti-6Al-4V/58S BG composites by using the selective laser melting method and systematically compared the effects of BG coating and doping in additively manufactured implants. In vivo histological results from animal tests were statistically analyzed and discussed in terms of osseointegration over 4- and 12-week periods. Bone-to-implant contact (BIC) and bone density (BD) were used as quantitative metrics to evaluate interactions between the implants and surrounding bone. Our findings indicate that both BG-doped and BG-coated implants accelerated bone ingrowth during the early stages of healing. BG-coated implants demonstrated a greater improvement than did pure 3D-printed Ti-6Al-4V implants. However, the effects of BG became nonsignificant during the later healing stage (12 weeks). This study provides a foundation for systematically investigating BG incorporation methods in 3D-printed biomedical implants and their effect on osseointegration.

physics.med-ph↗

arXiv2024

Automated Materials Discovery Platform Realized: Scanning Probe Microscopy of Combinatorial Libraries

Combinatorial materials libraries provide a powerful platform for mapping how physical properties evolve across binary and ternary cross-sections of multicomponent phase diagrams. While synthesis of such libraries has advanced since the 1960s and been accelerated by laboratory automation, their broader utility depends on rapid, quantitative measurements of composition-dependent structures and functionalities. Scanning probe microscopies (SPM), including piezoresponse force microscopy (PFM), offer unique potential for providing these functionally relevant, spatially resolved readouts. Here, we demonstrate a fully automated SPM framework for exploring ferroelectric properties across combinatorial libraries, focusing on binary Sm-doped BiFeO3 (SmBFO) and ternary Al$_{1-x-y}$Sc$_x$B$_y$N (Al,Sc,B)N systems. In SmBFO, automated exploration identifies the known morphotropic phase boundary with enhanced ferroelectric response and reveals a previously unreported double-peak fine structure. In the (Al,Sc,B)N library, ferroelectric behavior emerges at the phase-stability boundary, correlating with variations in morphology and defect concentration. By integrating automated SPM with wavelength-dispersive spectroscopy (WDS) and photoluminescence mapping, we resolve the composition-morphology-defect-property relationships underlying ferroelectric response and demonstrate a pathway toward a multi-tool, high-throughput characterization platform. Finally, we implement Gaussian-process-based single- and multi-objective Bayesian optimization to enable autonomous exploration, highlighting the Pareto front as a powerful framework for balancing competing physical rewards and accelerating data-driven physics discovery.

cond-mat.mtrl-sci↗

arXiv2023

Tunable Structural Transmissive Color in Fano-Resonant Optical Coatings Employing Phase-Change Materials

Reversible, nonvolatile, and pronounced refractive index modulation is an unprecedented combination of properties enabled by chalcogenide phase-change materials (PCMs). This combination of properties makes PCMs a fast-growing platform for active, low-energy nanophotonics, including tunability to otherwise passive thin-film optical coatings. Here, we integrate the PCM Sb2Se3 into a novel four-layer thin-film optical coating that exploits photonic Fano resonances to achieve tunable structural colors in both reflection and transmission. We show, contrary to traditional coatings, that Fano-resonant optical coatings (FROCs) allow for achieving transmissive and reflective structures with narrowband peaks at the same resonant wavelength. Moreover, we demonstrate asymmetric optical response in reflection, where Fano resonance and narrow-band filtering are observed depending upon the light incidence side. Finally, we use a multi-objective inverse design via machine learning (ML) to provide a wide range of solution sets with optimized structures while providing information on the performance limitations of the PCM-based FROCs. Adding tunability to the newly introduced Fano-resonant optical coatings opens various applications in spectral and beam splitting, and simultaneous reflective and transmissive displays, diffractive objects, and holograms.

physics.optics↗