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Neuromorphic photonics has recently emerged as a promising hardware accelerator, with significant potential speed and energy advantages over digital electronics for machine learning algorithms, such as neural networks of various types. Integrated photonic networks are particularly powerful in performing analog computing of matrix-vector multiplication (MVM) as they afford unparalleled speed and bandwidth density for data transmission. Incorporating nonvolatile phase-change materials in integrated photonic devices enables indispensable programming and in-memory computing capabilities for on-chip optical computing. Here, we demonstrate a multimode photonic computing core consisting of an array of programable mode converters based on on-waveguide metasurfaces made of phase-change materials. The programmable converters utilize the refractive index change of the phase-change material Ge 2 Sb 2 Te 5 during phase transition to control the waveguide spatial modes with a very high precision of up to 64 levels in modal contrast. This contrast is used to represent the matrix elements, with 6-bit resolution and both positive and negative values, to perform MVM computation in neural network algorithms. We demonstrate a prototypical optical convolutional neural network that can perform image processing and recognition tasks with high accuracy. With a broad operation bandwidth and a compact device footprint, the demonstrated multimode photonic core is promising toward large-scale photonic neural networks with ultrahigh computation throughputs. Integrated optical computing requires programmable photonic and nonlinear elements. The authors demonstrate a phase-change metasurface mode converter, which can be programmed to control the waveguide mode contrast, and build an optical convolutional neural network to perform image processing tasks.

Active learning—the field of machine learning (ML) dedicated to optimal experiment design—has played a part in science as far back as the 18th century when Laplace used it to guide his discovery of celestial mechanics. In this work, we focus a closed-loop, active learning-driven autonomous system on another major challenge, the discovery of advanced materials against the exceedingly complex synthesis-processes-structure-property landscape. We demonstrate an autonomous materials discovery methodology for functional inorganic compounds which allow scientists to fail smarter, learn faster, and spend less resources in their studies, while simultaneously improving trust in scientific results and machine learning tools. This robot science enables science-over-the-network, reducing the economic impact of scientists being physically separated from their labs. The real-time closed-loop, autonomous system for materials exploration and optimization (CAMEO) is implemented at the synchrotron beamline to accelerate the interconnected tasks of phase mapping and property optimization, with each cycle taking seconds to minutes. We also demonstrate an embodiment of human-machine interaction, where human-in-the-loop is called to play a contributing role within each cycle. This work has resulted in the discovery of a novel epitaxial nanocomposite phase-change memory material. Machine learning driven research holds big promise towards accelerating materials’ discovery. Here the authors demonstrate CAMEO, which integrates active learning Bayesian optimization with practical experiments execution, for the discovery of new phase- change materials using X-ray diffraction experiments.

Marked evolution of properties with minute changes in the doping level is a hallmark of the complex chemistry that governs copper oxide superconductivity as manifested in the celebrated superconducting domes and quantum criticality taking place at precise compositions 1 – 4 . The strange-metal state, in which the resistivity varies linearly with temperature, has emerged as a central feature in the normal state of copper oxide superconductors 5 – 9 . The ubiquity of this behaviour signals an intimate link between the scattering mechanism and superconductivity 10 – 12 . However, a clear quantitative picture of the correlation has been lacking. Here we report the observation of precise quantitative scaling laws among the superconducting transition temperature ( T c ), the linear-in- T scattering coefficient ( A 1 ) and the doping level ( x ) in electron-doped copper oxide La 2– x Ce x CuO 4 (LCCO). High-resolution characterization of epitaxial composition-spread films, which encompass the entire overdoped range of LCCO, has enabled us to systematically map its structural and transport properties with unprecedented accuracy and with increments of Δ x  = 0.0015. We have uncovered the relations T c ~ ( x c – x ) 0.5 ~ ( A 1 □ ) 0.5 , where x c is the critical doping in which superconductivity disappears and A 1 □ is the coefficient of the linear resistivity per CuO 2 plane. The striking similarity of the T c versus A 1 □ relation among copper oxides, iron-based and organic superconductors may be an indication of a common mechanism of the strange-metal behaviour and unconventional superconductivity in these systems. Precise quantitative scaling laws are observed between the normalized T -linear coefficient and T c among copper oxides, pnictides and a class of organic superconductors, suggesting a common underlying physics at work in these unconventional superconductors.

Data-centric applications are pushing the limits of energy-efficiency in today’s computing systems, including those based on phase-change memory (PCM). This technology must achieve low-power and stable operation at nanoscale dimensions to succeed in high-density memory arrays. Here we use a novel combination of phase-change material superlattices and nanocomposites (based on Ge 4 Sb 6 Te 7 ), to achieve record-low power density ≈ 5 MW/cm 2 and ≈ 0.7 V switching voltage (compatible with modern logic processors) in PCM devices with the smallest dimensions to date (≈ 40 nm) for a superlattice technology on a CMOS-compatible substrate. These devices also simultaneously exhibit low resistance drift with 8 resistance states, good endurance (≈ 2 × 10 8 cycles), and fast switching (≈ 40 ns). The efficient switching is enabled by strong heat confinement within the superlattice materials and the nanoscale device dimensions. The microstructural properties of the Ge 4 Sb 6 Te 7 nanocomposite and its high crystallization temperature ensure the fast-switching speed and stability in our superlattice PCM devices. These results re-establish PCM technology as one of the frontrunners for energy-efficient data storage and computing. Data-centric applications benefit from dense, low-power memory. Here the authors use a combination of chalcogenide superlattices and nanocomposites to achieve low switching voltage (0.7 V) and fast speed (40 ns) in 40-nm-scale phase-change memory.

Topological catalysts are special class of high-activity catalysts that have topological surface state with high-mobility electrons to promote electron transfer. Among topological materials, high-fold degenerate topological semimetals (TSMs) with chiral structures are particularly effective in hydrogen evolution reaction (HER) catalysis due to the larger energy window of nontrivial surface states and longer Fermi arcs than other classes. In this work, based on high-throughput calculations and the database of high-fold degenerate TSMs with chiral structures that we have established, we predict 16 high-activity topological catalysts with Gibbs free energy ∣ΔG∣ smaller than that of Pt. Among them, PtGa and PtPbTe with space group P2 1 3 and Pd 3 Pb 2 S 2 with space group I2 1 3 exhibit outstanding catalytic behavior. Furthermore, by comparing the adsorption energy on the surfaces with and without topological surface state in the same compound straightly, the substantive fact that the extremely long topological surface state provides virtual improvement of HER catalytic performance is verified. Thus, this work not only discovers a lot of HER topological catalysts, but also provides and corroborates an innovative strategy to design high-activity catalysts, i.e., constructing monometallic catalysts into TSMs. High-fold degenerate topological semimetals with chiral structures are promising candidates for catalyzing the hydrogen evolution reaction (HER). Here, the authors predict 16 high-activity topological catalysts with Gibbs free energies smaller than that of Pt, showing that long topological surface states can improve HER catalytic performance, particularly PtGa and PtPbTe.

We report a unique case involving a pure invasive micropapillary carcinoma (IMPC) of the nipple. At the same time, no cancer cells were found in other glands of the breast. There is no documented literature in the world on the micropapillary carcinoma of the nipple. A 43-year-old female detected a lump growing on her nipple since December 2015; however, she did not seek treatment before the lump enlarged. The patient presented at our hospital and underwent resection of the lump on 14 December 2016. Pathologic examination of the surgical specimen revealed IMPC involving the nipple. The patient underwent unilateral radical surgery. The pathologic examination of the specimen revealed normal breast tissue without cancer cells. Herein, we discuss the characteristics, diagnosis, and treatment of IMPC, hoping to provide further insight for clinicians and pathologists.

High‐throughput experimental approaches to rapidly develop new materials require high‐throughput data analysis methods to match. Spectroscopic ellipsometry is a powerful method of optical properties characterization, but for unknown materials and/or layer structures the data analysis using traditional methods of nonlinear regression is too slow for autonomous, closed‐loop, high‐throughput experimentation. Herein, three methods (termed spectral, piecewise, and pointwise) of spectroscopic ellipsometry data analysis based on deep learning are introduced and studied. After initial training, the incremental time for inferring optical properties can be a thousand times faster than traditional methods. Results for multilayer sample structures with optically isotropic materials are presented, appropriate for high‐throughput studies of thin films of phase‐change materials such as GeSbTe (GST) alloys. Results for studies on highly birefringent layered materials are also presented, exemplified by the transition metal dichalcogenide MoS2. How the materials under test and the experimental objectives may guide the choice of analysis methods are discussed. The utility of our approach is demonstrated by analyzing data measured on a composition spread of GeSbTe phase‐change alloys containing 177 distinct compositions, and identifying the composition with optimal phase‐change figure of merit in only 1.4 s of analysis time. Spectroscopic ellipsometry is a powerful and data‐rich metrology to characterize materials, devices, and manufacturing processes. However, traditional data analysis tends to be too slow for rapid feedback. Deep learning methods are developed to quickly and accurately analyze spectroscopic ellipsometry data. The efficacy of the methods are demonstrated using data from high‐throughput synthesis of phase‐change materials for photonics.

Chemical vapor deposition (CVD) is one of the most versatile techniques for the controlled synthesis of functional nanomaterials. When multiple precursors are induced, the CVD process often gives rise to the growth of doped or alloy compounds. In this work, we demonstrate the self-assembly of a variety of ‘phase-separated’ functional nanostructures from a single CVD in the presence of various precursors. In specific, with silicon substrate and powder of Mn and SnTe as precursors, we achieved self-organized nanostructures including Si/SiO x core-shell nanowire heterostructures both with and without embedded manganese silicide particles, Mn 11 Si 19 nanowires, and SnTe nanoplates. The Si/SiO x core-shell nanowires embedded with manganese silicide particles were grown along the direction of the crystalline Si via an Au-catalyzed vapor-liquid-solid process, in which the Si and Mn vapors were supplied from the heated silicon substrates and Mn powder, respectively. In contrast, direct vapor-solid deposition led to particle-free -oriented Si/SiO x core-shell nanowires and -oriented Mn 11 Si 19 nanowires, a promising thermoelectric material. No Sn or Te impurities were detected in these nanostructures down to the experimental limit. Topological crystalline insulator SnTe nanoplates with dominant {100} and {111} facets were found to be free of Mn (and Si) impurities, although nanoparticles and nanowires containing Mn were found in the vicinity of the nanoplates. While multiple-channel transport was observed in the SnTe nanoplates, it may not be related to the topological surface states due to surface oxidation. Finally, we carried out thermodynamic analysis and density functional theory calculations to understand the ‘phase-separation’ phenomenon and further discuss general approaches to grow phase-pure samples when the precursors contain residual impurities.

Background Mastoscopic surgery is proven to have lower incidence of postoperative complications and better postoperative recovery than traditional breast cancer surgery. This study aimed to examine the feasibility of mastoscopic modified radical mastectomy (MRM) with skin nipple-areola preservation under air cavity-free suspension hook and stage I silicone prosthesis implantation (SMALND) compared with routine MRM. Methods This was a retrospective study of patients who underwent MRM for breast cancer at the Shengjing Hospital Affiliated to China Medical University between January 1, 2019, and June 30, 2019. Surgical outcomes, complications, satisfaction, and quality of life (Functional Assessment of Cancer Therapy-Breast [FACT-B] [Chinese version]) were compared between the two groups. Results A total of 87 patients were enrolled, with 30 underwent SMALND and 57 underwent routine MRM. The intraoperative blood loss in the SMALND group was lower than in the control group (165.3±44.1 vs. 201.4±52.7 ml, P =0.001), the operation time was longer (220.5±23.9 vs. 155.6±9.2 min, P <0.001), daily axillary drainage volume was smaller (20.2±3.6 vs. 24.1±3.0 ml, P <0.001), daily subcutaneous drainage volume was smaller (15.5±2.3 vs. 19.3±3.5 ml, P <0.001), the discharge time was shorter (7.5±1.6 vs. 9.0±1.8 days, P <0.001), and FACT-B scores were higher (83.8±5.6 vs. 72.1±4.6, P <0.001). The overall satisfaction was higher in the SMALND group than in the controls (76.7% vs. 54.4%, P =0.041). Compared with the controls, the occurrence rates of nipple and flap necrosis, upper limb edema, and paraesthesia in the SMALND group were lower within 6 months (all P <0.05). Conclusions Compared with traditional MRM, SMALND had better surgical outcomes, higher satisfaction, higher quality of life, and lower complication rates.

Modern machine learning and autonomous experimentation schemes in materials science rely on accurate analysis of the data ingested by these models. Unfortunately, accurate analysis of the underlying data can be difficult, even for domain experts, complicating the training of the models intended to drive experiments. This is especially true when the goal is to identify the presence of weak signatures in diffraction or spectroscopic datasets. In this work, we examine a set of as-obtained diffraction data that track the phase transition from monoclinic to tetragonal in a Nb-doped VO2 film as a function of temperature and dopant concentration. We then task a set of domain experts and a set of machine learning experts with identifying which phase is present in each diffraction pattern manually and algorithmically, respectively; in both cases, the labels can vary dramatically, especially at the phase boundaries. We use the mode of the labels and the Shannon entropy as a method to capture, preserve and propagate consensus labels and their variance. Further we use the expert labels as a benchmark and demonstrate the use of Shannon entropy weighted scoring to test the performance of machine learning generated labels. Finally, we propose a material data challenge centered around generating improved labeling algorithms. This real-world dataset curated with expert labels can act as test bed for new algorithms. The raw data, annotations and code used in this study are all available online at data.gov and the interested reader is encouraged to replicate and improve the existing models