py4DSTEM: A Software Package for Four-Dimensional Scanning Transmission Electron Microscopy Data Analysis.

Scanning transmission electron microscopy (STEM) allows for imaging, diffraction, and spectroscopy of materials on length scales ranging from microns to atoms. By using a high-speed, direct electron detector, it is now possible to record a full two-dimensional (2D) image of the diffracted electron beam at each probe position, typically a 2D grid of probe positions. These 4D-STEM datasets are rich in information, including signatures of the local structure, orientation, deformation, electromagnetic fields, and other sample-dependent properties. However, extracting this information requires complex analysis pipelines that include data wrangling, calibration, analysis, and visualization, all while maintaining robustness against imaging distortions and artifacts. In this paper, we present py4DSTEM, an analysis toolkit for measuring material properties from 4D-STEM datasets, written in the Python language and released with an open-source license. We describe the algorithmic steps for dataset calibration and various 4D-STEM property measurements in detail and present results from several experimental datasets. We also implement a simple and universal file format appropriate for electron microscopy data in py4DSTEM, which uses the open-source HDF5 standard. We hope this tool will benefit the research community and help improve the standards for data and computational methods in electron microscopy, and we invite the community to contribute to this ongoing project.

Closed-loop optimization of fast-charging protocols for batteries with machine learning.

Simultaneously optimizing many design parameters in time-consuming experiments causes bottlenecks in a broad range of scientific and engineering disciplines1,2. One such example is process and control optimization for lithium-ion batteries during materials selection, cell manufacturing and operation. A typical objective is to maximize battery lifetime; however, conducting even a single experiment to evaluate lifetime can take months to years3-5. Furthermore, both large parameter spaces and high sampling variability3,6,7 necessitate a large number of experiments. Hence, the key challenge is to reduce both the number and the duration of the experiments required. Here we develop and demonstrate a machine learning methodology  to efficiently optimize a parameter space specifying the current and voltage profiles of six-step, ten-minute fast-charging protocols for maximizing battery cycle life, which can alleviate range anxiety for electric-vehicle users8,9. We combine two key elements to reduce the optimization cost: an early-prediction model5, which reduces the time per experiment by predicting the final cycle life using data from the first few cycles, and a Bayesian optimization algorithm10,11, which reduces the number of experiments by balancing exploration and exploitation to efficiently probe the parameter space of charging protocols. Using this methodology, we rapidly identify high-cycle-life charging protocols among 224 candidates in 16 days (compared with over 500 days using exhaustive search without early prediction), and subsequently validate the accuracy and efficiency of our optimization approach. Our closed-loop methodology automatically incorporates feedback from past experiments to inform future decisions and can be generalized to other applications in battery design and, more broadly, other scientific domains that involve time-intensive experiments and multi-dimensional design spaces.

Network analysis of synthesizable materials discovery.

Assessing the synthesizability of inorganic materials is a grand challenge for accelerating their discovery using computations. Synthesis of a material is a complex process that depends not only on its thermodynamic stability with respect to others, but also on factors from kinetics, to advances in synthesis techniques, to the availability of precursors. This complexity makes the development of a general theory or first-principles approach to synthesizability currently impractical. Here we show how an alternative pathway to predicting synthesizability emerges from the dynamics of the materials stability network: a scale-free network constructed by combining the convex free-energy surface of inorganic materials computed by high-throughput density functional theory and their experimental discovery timelines extracted from citations. The time-evolution of the underlying network properties allows us to use machine-learning to predict the likelihood that hypothetical, computer-generated materials will be amenable to successful experimental synthesis.

Graphene-Based Thermopile for Thermal Imaging Applications.

In this work, we leverage graphene's unique tunable Seebeck coefficient for the demonstration of a graphene-based thermal imaging system. By integrating graphene based photothermo-electric detectors with micromachined silicon nitride membranes, we are able to achieve room temperature responsivities on the order of ~7-9 V/W (at λ = 10.6 µm), with a time constant of ~23 ms. The large responsivities, due to the combination of thermal isolation and broadband infrared absorption from the underlying SiN membrane, have enabled detection as well as stand-off imaging of an incoherent blackbody target (300-500 K). By comparing the fundamental achievable performance of these graphene-based thermopiles with standard thermocouple materials, we extrapolate that graphene's high carrier mobility can enable improved performances with respect to two main figures of merit for infrared detectors: detectivity (>8 × 10(8) cm Hz(1/2) W(-1)) and noise equivalent temperature difference (<100 mK). Furthermore, even average graphene carrier mobility (<1000 cm(2) V(-1) s(-1)) is still sufficient to detect the emitted thermal radiation from a human target.

Photoresponse of an electrically tunable ambipolar graphene infrared thermocouple.

We explore the photoresponse of an ambipolar graphene infrared thermocouple at photon energies close to or below monolayer graphene's optical phonon energy and electrostatically accessible Fermi energy levels. The ambipolar graphene infrared thermocouple consists of monolayer graphene supported by an infrared absorbing material, controlled by two independent electrostatic gates embedded below the absorber. Using a scanning infrared laser microscope, we characterize these devices as a function of carrier type and carrier density difference controlled at the junction between the two electrostatic gates. On the basis of these measurements, conducted at both mid- and near-infrared wavelengths, the primary detection mechanism can be modeled as a thermoelectric response. By studying the effect of different infrared absorbers, we determine that the optical absorption and thermal conduction of the substrate play the dominant role in the measured photoresponse of our devices. These experiments indicate a path toward hybrid graphene thermal detectors for sensing applications such as thermography and chemical spectroscopy.