Industrial production of graphene by chemical vapor deposition (CVD) requires more than the ability to synthesize large domain, high quality graphene in a lab re- actor. The integration of graphene in the fabrication process of electronic devices re- quires the cost-e ective and environmentally-friendly production of graphene on dielec- tric substrates, but current approaches can only produce graphene on metal catalysts. Sustainable manufacturing of graphene should also conserve the catalyst and reaction gases, but today the metal catalysts are typically dissolved after synthesis. Progress toward these objectives is hindered by the hundreds of coupled synthesis parameters that can strongly a ect CVD of low-dimensional materials, and poor communication in the published literature of the rich experimental data that exists in individual lab- oratories. We report here on a platform, \Graphene { Recipes for synthesis of high quality material" (Gr-ResQ: pronounced graphene rescue), that includes powerful new tools for data-driven graphene synthesis. At the core of Gr-ResQ is a crowd-sourced database of CVD synthesis recipes and associated experimental results. The database captures300 parameters ranging from synthesis conditions like catalyst material and preparation steps, to ambient lab temperature and reactor details, as well as result- ing Raman spectra and microscopy images. These parameters are carefully selected to unlock the potential of machine-learning models to advance synthesis. A suite of associated tools enable fast, automated and standardized processing of Raman spectra and scanning electron microscopy images. To facilitate community-based e orts, Gr- ResQ provides tools for cyber-physical collaborations among research groups, allowing experiments to be designed, executed, and analyzed by di erent teams. Gr-ResQ also allows publication and discovery of recipes via the Materials Data Facility (MDF), which assigns each recipe a unique identi er when published and collects parameters in a search index. We envision that this holistic approach to data-driven synthesis can accelerate CVD recipe discovery and production control, and open opportunities for advancing not only graphene, but also many other 1D and 2D materials. Keywords: Graphene, robotic synthesis, materials data, recipes, machine learning, Raman 3 spectroscopy, nanomanufacturing Introduction Owing to its versatile electronic properties and atomic thinness, graphene has applications spanning electronic devices, sensors, and transparent electrodes.1{5Most such applications require high-throughput, controllable manufacture to enable industrially relevant use. To meet the requirements of electronic device fabrication, graphene synthesis needs to be very tightly controlled, leading to predictable and repeatable layer count and defect density. For example, synthesis via chemical vapor deposition (CVD) uses metal catalysts that are typically dissolved after synthesis, which is not only uneconomical but also unsustainable due to the acids used in dissolving. Moreover, the transfer of graphene from the metal catalyst to dielectric substrates, which is needed for electronic device functionality, creates many defects, hindering its use in commercial applications. The ideal synthesis process should produce high quality graphene on dielectric substrates, an outstanding objective notwithstanding the current progress.6{8 Yet despite focused e orts from the research community, breakthroughs in synthesis are infrequent. New methods are needed to accelerate scienti c discovery around CVD growth of graphene. Two challenges must be addressed: (i) the sensitivity of the resulting material to growth parameters, and (ii) the dispersed knowledge available at the level of individual research groups, combined with a culture of incomplete communication of synthesis param- eters when reporting in the literature. Figure 1(a) shows examples of scanning electron microscopy images (SEM) of graphene growth from a small number of experiments using ap- proximately the same recipe. Understanding these images requires deep expertise, not only to identify features in the images, but to establish causality given the extreme sensitivity of the growth process. To address these challenges, we have developed the platform \Graphene Recipes for synthesis of high Quality materials" (Gr-ResQ, pronounced graphene rescue)9 4 to enable community-scale sharing of recipes for CVD graphene synthesis. Figure 1(b) shows a timeline of past milestones and future goals for graphene synthesis via CVD. After the initial scotch tape exfoliation and discovery of graphene in 2004,10 research groups around the world shifted their attention gradually from carbon nanotubes to this new exciting material. Fortunately, the reactor designs, growth conditions, and gases for both are similar. The general idea of the CVD recipe is similar in graphene to CNTs (Figure 1(c)). Synthesis of multilayered graphene on nickel was soon followed by the synthesis of single layers of graphene on copper in 2009 (Figure 1(b)). Large amounts of carbon can be dissolved in nickel, with most growth taking place during cooling in the form of precipitation and segregation. On the other hand, copper has low carbon solubility, which results in synthesis that is governed by surface adsorption. Mixing copper and nickel in carefully selected ratios within the catalyst, combined with uid mechanics engineering in the reactor, led to the production of large domain sizes exceeding a square inch of high quality graphene in 201611and 2019.12Note that it took around a decade to increase the domain size from microns to tens of millimeters. As of the beginning of 2020, there are already more than 12,000 publications per year with the words \graphene synthesis" in the title, abstracts, or keywords: see Figure 1(d). The graphene CVD process, illustrated schematically in Figure 1(c), is typically divided into three stages: annealing, growth, and cooling. During the annealing step, a substrate is heated in order to prepare it for growth. During the growth phase, carbon-containing precursors are added, allowing the carbon atoms to crystallize on the substrate. In the - nal cooling step, the material is brought back down to room temperature. In some CVD processes, growth also occurs in this latter step. While this procedure may seem straight- forward, there are hundreds of tuning parameters involved, resulting in a hyper-dimensional recipe phase space. While this can also be true for other chemical synthesis processes, CVD of atomically thin materials remains among the most challenging. First, the volume of mate- rial produced is small compared to the reactor size (ratio of 109), which makes synthesis 5 sensitive to minor local variations in the growth chamber. When two chemical processes are taking place { such as crystallization and etching, with the latter often associated with the presence of uncontrolled amounts of oxygen or hydrogen { the thin nature of the material can entail greater sensitivity to the deleterious consequences of the competing process. Sec- ondly, while wet chemical synthesis bene ts from mixing of the reaction products, atomically thin materials have limited mobility after they crystallize on the surface. Thirdly, gas ow mechanics and heat transfer can a ect the kinetics of the monolayer synthesis, altering the results in unpredictable ways. It is often said that graphene synthesis is so sensitive that it may be a ected `when a researcher sneezes in the lab'! Unfortunately, the tedious trial and error needed to develop synthesis recipes is an accepted reality, and synthesis research bene ts only qualitatively from physical modeling and simulations. The incomplete nature of synthesis parameters reported in published literature hinders progress towards manufacturing goals in several ways. First, it makes replicating a given growth experiment challenging since all parameters needed to precisely repeat the recipe are not available. For instance, the furnace setup itself a ects the graphene crystal size and nucleation density by in uencing the uid ow, heat transfer, and growth kinetics. However, the total ow rate is often not reported (typically, only ratios between reaction gas ow rates are); nor are the tube dimensions, heated zone length, or ow resistance across the tube. Other important yet often ignored parameters are the reactor dew point and oxygen content. The dew point in particular is typically an 'uncontrolled' parameter, and usually not measured despite its critical role. Its role has rst been discovered for carbon nanotubes: within a narrow range, varying dew points can produce single-walled or multi-walled carbon nanotubes (CNT), or even completely inhibit CNT growth.13For graphene, water vapor and oxygen in the furnace or reaction gas a ect nucleation, defect density, and growth rate.14{17 Minute oxygen content can be present in the form of impurities/surface oxides in the catalyst, or neighboring oxides in the reactor. While measuring local variations in oxygen or hydrogen content on the catalyst surface during the reaction is currently not tenable, dew point can 6 Figure 1: (a) Examples of scanning electron microscopy (SEM) images of graphene synthe- sized by CVD (images acquired by the authors). (b) Past graphene milestones and future goals.10{12(c) Schematic illustration of a typical graphene CVD synthesis recipe. (d) Number of relevant publications since graphene's discovery. (e) Percentage of unreported information pertaining to seven phases in the synthesis process, collected from a list of 21 highly cited publications related to graphene synthesis. (f) Top seven most unreported parameters from same publications as (e). 7 be measured upstream or in the ambient lab environment itself. Yet not only are recipe parameters like dew point or oxygen content often ignored in the scienti c literature, so too are characterizations of the products of recipes such as graphene coverage area, nucleation density or domains per area, and growth rate. Even those growth conditions that are reported are scattered within articles and their supporting online information, making it easy to miss important parameters. This incomplete data in the scienti c literature also prevents the use of data-driven or machine-learning tools across consolidated sets of growth experiments. Almost without exception, only the conditions that successfully yielded graphene growth are reported. Negative results are important, since there are narrow windows of synthesis conditions that give rise to the best results, with deviations from these windows dramatically a ecting the products. To quantitatively illustrate the dearth of CVD parameters in published articles, we tabu- lated 29 parameters of synthesis and characterization from 21 of the most cited publications. These articles were identi ed via query from the ISI Web of Knowledge for \graphene syn- thesis using chemical vapor deposition",18{38and analyzed to quantify missing data for each of the 29 parameters (analysis presented in SI). Figure 1(e) displays the top seven parame- ters crucial to reproducibility that were most frequently unreported. Figure 1(f) groups the missing data into seven categories of parameters a ecting graphene synthesis: experimental setup (e.g., furnace design details and ambient conditions) growth results (description of graphene produced including sample coverage, domain size, nucleation density) cooling parameters ( ow rates, cooling rates) sample information (catalyst preparation, oxygen content) annealing parameters (exact ow rates versus time, temperature and pressure) growth parameters (exact ow rate versus time, temperature and pressure) 8 characterization (details about growth uniformity and sample morphology based on imaging and Raman spectra) The most missing data appears in the experimental setup category and includes parame- ters such as the furnace design, whose characteristics are known to have an a ect on ow velocities, heat transfer, and ambient humidity. The Gr-ResQ platform introduced here has been designed to overcome several of these challenges. The platform is accessible online through nanoHUB,39and consists of several features: (i) a repository containing recipes and their associated experimental results that users can search and query, visualize, analyze, and submit to, in order to learn from the aggregate knowledge of the community; (ii) a set of tools for analysis of Raman spectra (to determine graphene quality) and scanning electron microscopy images (to estimate growth coverage and domain orientation); (iii) the capability to apply machine learning methods to the aggregate data; (iv) an interface with the \Operating System for Cyberphysical Manufac- turing" (OSCM, pronounced awesome), which allows users to make requests for collaborators to run user-generated experiments and characterize results; and (v) full Gr-ResQ datasets that are published periodically to the Materials Data Facility (MDF)40,41where they are archived and assigned a digital object identi er (DOI), and where collected parameters are logged in a search index to promote discovery. As a whole, the platform enables community networking, and facilitates an iterative, community-driven learning procedure. Similar plat- forms for disseminating materials data have already seen success in other elds. In the area of polymer nanocomposites, NanoMine has been established as a platform for document- ing and analyzing material properties.42In the theoretical space, Materials Project43and OQMD44provide access to large troves of material properties derived from high-throughput density functional theory simulations. Within an individual research group, Gr-ResQ can be used to track and document synthesis conditions and results. Across the community, it can augment the collective talent and resources of researchers across the world. Gr-ResQ also adheres to the FAIR data principles.45For data to be considered FAIR, they 9 should be Findable, Accessible, Interoperable, and Reproducible. In this work, the individual data and complete datasets are both ndable and accessible for free to the public through the Gr-ResQ interface as well as via Materials Data Facility web and programmatic interfaces. We have also worked to make the data interoperable, reproducible, and indeed extendable by de ning MDF schemas that describe and represent key elements of the processing, structure, and property results captured by the experiments. These schemas are openly available for reuse, and we also reuse schemas where possible (e.g., DataCite to represent general author information, descriptions, and literature linkages and NIST material-speci c schemas to represent high level aspects of the experiments). We see the work here as critical to achieving fully reproducible work, across labs, in the graphene synthesis community. Overview Figure 2 illustrates the components of Gr-ResQ. It consists of the database (shown on the right), connected to the various tools (on the left). Users can launch these tools directly from nanoHUB to upload, analyze, and post-process their own graphene data. As illustrated in Figure 3(a), we divide the data into six categories: (1) provenance (author, institution, etc..), (2) recipe (annealing, growth and cooling parameters), (3) other synthesis conditions (furnace setup, catalyst sample details, preparation steps, known contamination, ambient conditions), (4) growth results (user-reported graphene domain size, nucleation density, coverage), (5) characterization data (raw images, Raman spectra), and (5) analyses (useful information extracted using the image and Raman analysis tools such as in-plane orientation of graphene domains, Raman peak locations and intensity ratio). The database currently contains both data from the authors as well as data from the literature.20,22,24,27,28,30,32 The submit tool handles data ingestion into the database, uploading recipes, raw images (png, ti , formats), and Raman spectra (txt). The query tool allows users to search and visualize existing data. Query functionality is designed to be more e ective compared to a 10 Figure 2: The Gr-ResQ platform has three main components: the submission tool, the query tool and the analysis tools. Raw data are fed into the database with the submission tool and derived data are added using the analysis tools. These data are then validated and the resulting dataset is published to MDF. The submission tool is also compatible with OSCM to allow users to generate and test new recipes. 11 typical literature search engine. It allows users to search for recipes directly by specifying parameter lters, such as a growth temperature higher than a speci c value, or graphene with a speci c Raman peaks ( G0=G) ratio. This query functionality can eliminate the arduous process of combing the literature. To establish a consistent analysis of growth results, users can post-process the graphene scanning electron microscopy (SEM) images and Raman les, using the custom developed analysis tools. The analysis tool set includes an application that can t Raman spectra in order to determine graphene quality and layer count. It also includes an image tool that can apply masks and image processing algorithms to separate the graphene from the substrate. Gr-ResQ uses the Materials Data Facility (MDF), a set of general materials data publi- cation and discovery services, and the Operating System for Cyberphysical Manufacturing (OSCM). MDF provides a stable data archive platform on which to publish periodic releases of Gr-ResQ data and through which users can query and nd the Gr-ResQ data. Each data release is a static snapshot of the entire Gr-ResQ database tagged with a DOI. OSCM allows users to crowdsource their experiments by providing instructions to participating scientists, who then manufacture the material and return the associated data. The Gr-ResQ interface allows users to build a step-by-step procedure and submit it to one or more participating labs as an OSCM transaction. An OSCM transaction is completed when the associated experimental data has been uploaded. The Gr-ResQ platform is developed in Python using the cross-platform PyQt library built on the popular Qt framework to generate graphical user interfaces. PyQt combined with the pyqtgraph plotting package provide the exibility to develop a host of widgets and tools for data manipulation. On the backend (as of version 1.3.0) we use a MySQL database and the SQLAlchemy toolkit are used to manage recipe data. Finally, our tools are hosted on nanoHUB,39a platform for both students and researchers, providing learning materials, software tools and tools for collaboration. nanoHUB provides a single access point for Gr- ResQ with the computing resources and support to host the tools as well as functionality to 12 manage member access and privileges. Users can run the Gr-ResQ application directly from nanoHUB with no need to install software locally. We anticipate four types of Gr-ResQ users: Querier/analyzer - In the basic use case, this user has open access to the tools and database and has privileges that permit read-only access. This user can search the database for relevant data and visualize the results to facilitate future synthesis exper- iments. They can also make use of the analysis tool suite to post-process their own data. Submitter - A user with writing privileges, who can add new recipes to the database. Validator - A core administrator who can verify and validate submitted recipes to ensure data integrity. OSCM user - A user who can request experiments through the OSCM interface. Submit Tool The submit tool enables users to contribute recipes to the Gr-ResQ database. It uses multi- tab organization for users to sequentially add their data. The tool contains tabs for Prepa- ration, Properties, File Upload, and Provenance. The Preparation tab, pictured in Figure 3(b), allows users to input the experimental conditions for their recipe and add any number of annealing, growing, or cooling steps. To ease work ow, desired input units can be selected for each parameter. The submit interface also permits users to directly construct recipes for OSCM submission. A user simply inputs their desired recipe and the tool will construct and submit the recipe to OSCM (described further in Integration with OSCM). Analysis information, such as coverage and number of layers, can be entered in the Properties tab. The File Upload tab allows users to upload associated SEM images and Raman spectroscopy data. Uploaded Raman data are automatically scanned and G,G0, andDpeak positions 13 (a) (b) Figure 3: (a) Types of data contained within Gr-ResQ. (b) The recipe submission interface. Users input recipe procedure step-by-step. and amplitudes are calculated. These peaks provide information for users to measure the graphene's quality. For instance, the ratio G0=Gis correlated to the number of layers. Users also correlate the uploaded Raman spectra with the sample uniformity by specifying the frac- tion of the sample which is represented by the uploaded spectrum. This allows our database to determine average values of Raman metrics derived from the peak amplitudes for each sample. Lastly, users can provide their name and aliation in the Provenance tab, so that each recipe can be associated with its author. The data are then reviewed by the user and submitted to the database. Upon submission, the tool automatically checks for invalid entries. The checks include: Checking for author name and institution Checking for preparation input Checking for a base pressure 14 Ensuring all preparation steps have a corresponding temperature, pressure, and dura- tion Ensuring all growth steps have a carbon source and ow rate Checking for proper Raman spectroscopy format and corresponding characteristic per- centage Should any of these tests fail, the user is noti ed so that they can correct their input. Should the submission be valid, any other user with validation privileges need only provide a nal validation for the recipe to be visible to all users. Query Tool The query tool is the access point to the Gr-ResQ database, providing search and visual- ization capability. With the query tool, users can perform searches on attributes related to the synthesis recipes (temperature, catalyst, etc..), the growth results (number of layers, coverage, etc.), or graphene properties (Raman peaks characteristics). An example search is illustrated in Figure 4(a): the rows under `Results' become populated with all database recipes that match the query (in this case, all recipes that use palladium thin lm as a catalyst), A given recipe can be previewed in the preview pane (Figure 4(b)) by clicking the entry in the Results table. The preview pane is split into six tabs: Details, SEM, Raman, Recipe, Provenance, and Admin. The Details tab shows information regarding the properties that were entered with the recipe and general information about the experimental conditions (tube size, catalyst, etc.). SEM images and associated masks corresponding to regions of the image classi ed as graphene or substrate can be found in the SEM tab, while the Raman tab shows plots of associated Raman data. The Recipe tab allows users to visualize the synthesis parameters such as temperature, pressures or gas ow rates versus time. An example recipe visualization is illustrated in Figure 4(b). Finally, the Provenance tab contains the name and aliation of the recipe contributor. 15 (a) (b) Figure 4: (a) An example search using the query interface to lter recipes that use palladium leaf as a catalyst. (b) An example recipe visualization of the recipe's furnace temperature. 16 (a) (b) (c) Figure 5: The query tool allows users to visualize sets of recipe data as conventional plots or t-SNE plots (a) Example of conventional plotting allowing direct comparison of synthesis criteria to output. (b) Schematic representation of a t-SNE calculation. Low dimensional visualizations can be used to look for clusters of synthesis parameters that correlate with a desired output. (c) Example of t-SNE plot for 13 parameters colored according to maximum pressure.17 Analysis and Plotting To enable statistical analysis and machine learning of Gr-ResQ data, the \Plotting" and `t-SNE' tabs within the Query Tool allows users to visualize cor- relations between parameters in the recipe data. In Figure 5, two methods of visualization are illustrated. The `Plotting' tab allows users to construct XY scatter plots from data. For instance, Figure 5(a) shows how the G' to G Raman peak correlates to the carbon ow rate across the queried data. The t-distributed stochastic neighbor embedding (t-SNE) tab allows search for clusters of data that correlate to a selected metric. A schematic representation of this can be seen in 5(b). t-SNE is a dimensionality reduction algorithm that seeks to preserve, in low dimen- sional space, the relative similarity between points in high dimensional space: points that appear close to each other on the plot are more similar in their attributes. Dimensionality reduction is accomplished by minimizing the Kullback-Leibler divergence between a con- structed Gaussian joint probability distribution of high dimensional data and a 'Student-t' distribution of points in low dimensional space.46The positions of the data points in re- duced dimensional space are optimized to best represent clusters of similar data in the high dimensional space. These low dimensional points can then be colorized according to a de- sired metric to visualize graphene properties, to look for areas of recipe phase space that correlate to a desired metric. For example, in Figure 5(c) each data point corresponding to a particular recipe has been colorized based on its maximum pressure. t-SNE enables visual inspection and search for patterns in recipes that potentially contain tens or hundreds of attributes. A challenge to obtaining meaningful information from t-SNE is that some recipes may be missing input for selected attributes. Consequently, it can be dicult to know which sets of features have sucient support. Gr-ResQ allows users to rst determine the fraction of rows that contain valid data for a particular set of features. This fraction is referred to as support and is de ned as: supp (F) =jfd2DjFdgj jDj; (1) 18 wheredrepresents the set of features that are valid for a particular row, Dis the set of dfor all rows and Fis the chosen set of features. Using the apriori algorithm, the tool determines the support for all potential feature sets across the selected dataset. Using the t-SNE widget, users can then choose a set of features according to a desired level of support or choose a set of features manually. Figure 5(c) shows a t-SNE plot for 12 parameters with a support of 0.8. The distances between two points re ect their relative similarities in the 12- dimensional space. A clustering pattern is observed when the data are colored according to the maximum pressure. Experiments with similar maximum pressures tend to be clustered together in higher dimensional space, likely due to similar experimental procedures in each of the clusters. Image Tool Figure 6: The work ow of a typical user of the image tool to isolate the graphene from the substrate in an SEM image and then generate a corresponding mask for further use. The user rst uploads the SEM image and crops the image to remove the scale and then uses the masking tool to isolate the graphene from substrate. The Image Tool is an image processing platform customized for analysis of SEM images 19 of graphene, with potential applicability to other types of images as well.47SEM images are typically used to observe the growth results of a synthesis experiment, such as areal coverage, nucleation density, and the shape, size, and quality of graphene domains. While visual inspection of images can sometimes be sucient to determine the quality of graphene, it is desirable to determine quantitative metrics as well. Quantitative metrics can provide for easier comparison between experimental results and are useful as response variables when attempting to predict optimal recipes. Such metrics include the graphene coverage, which measures the fraction of the substrate that contains graphene and average domain size. The image tool provides the capability to estimate these metrics in an automated manner. Image Segmentation . To calculate the areal coverage of graphene in an image, the pix- els need to be classi ed as corresponding to graphene or non-graphene. The work ow for separating graphene from the substrate comprises a three-step process as illustrated in Fig- ure 6. A user uploads a grayscale image to the program, removes unnecessary parts of the image (frame, scale bar, etc), and then creates a \mask" for the area containing graphene. Our tool provides cropping and erasing functionality to remove parts of the image manually, but also has the ability to automatically remove scale bars from the image as well. After isolating the relevant part of the image, the last step is to distinguish pixels corresponding to graphene from those of the background substrate, which can be challenging for images with complex features. Within an SEM image of synthesized graphene, there is typically a mix of graphene (if present), substrate, and random substrate features such as contam- inant particles. Depending on the microscope and the synthesis results, SEM images can look quite di erent. The shape and color of the graphene might vary, as can the contrast between the graphene and substrate. Consequently, simply ltering based on pixel intensity does not work in many instances. Instead, our tool uses a template matching algorithm, as implemented by OpenCV,48to do the bulk of the classi cation. Since trained scientists can simply look at images and identify graphene, in the image tool users can select one or more portions of the image containing graphene to act as \templates." These templates exhibit 20 features on the pixel level that are consistent wherever graphene is present in the image. The tool scans the image for regions that look like the template according to a selected similarity metric. The default similarity metric is R(x;y) =  x0;y0(T(x0;y0)I(x+x0;y+y0))2; (2) whereTis a template selected by the user from the image and Iis the image. Each pixel is thus assigned a value corresponding to this measure. The user can use a sliding bar to select a cuto threshold to determine the maximum value for determining whether a pixel matches the template (i.e., corresponds to graphene). Since the synthesis of aligned graphene crystals (single crystal-like) is often desired, we have added the capability to measure the angular alignment of graphene domains. For instance, many experiments use relatively short synthesis times to obtain large domains of \unstitched" single-crystal graphene domains, which would appear close to hexagonal in shape. For short synthesis times yielding isolated domains of graphene that are not joined, angular alignment measurement allows observation of distinct domain edges. The domains are often not perfectly hexagonal in shape due to dislocations or other types of defects in their structure. The image tool includes a Sobel lter, which generates a histogram of the distribution of edge angles in the image. This approach is based on convolving the grayscale image with Sobel kernels, which approximate the derivatives of the pixel intensities Gxand Gyin the x-direction and y-direction respectively. The magnitude and direction of the gradient at each pixel is approximated as G=q G2 x+G2 y = tan1Gy Gx ; whereGis the gradient magnitude and is the gradient direction. The distribution of the alignment of the domains can be obtained by binning the gradient directions weighted by 21 their magnitudes in a histogram. Perfectly aligned domains will show a comb function, with a periodicity of 60in the histogram. Figure 7 illustrates an arti cially constructed example to demonstrate. In the Figure 7(a), the hexagons are oriented about the central angle with noise added. After applying the Sobel lter and binning the angles weighted by their magnitude G, a periodic pattern of peaks emerge in the resulting histogram. These peaks correspond to the average orientation of the edges of the hexagons, with the variance of the peaks arising from the distribution of orientations as well as error induced by aliasing. (By contrast, randomly oriented graphene would have no clearly discernible peaks as the edges would be uniformly distributed.) We can then convolve this histogram with a comb with a periodicity of 60and re-center the resulting convolution so that its center of mass is situated at 30. The variance of the convolution indicates the quality of the graphene, where lower variances correspond to greater domain alignment. Figure 7(b) illustrates the process for hexagons with three di erent respective orientations scattered about the image. Correspondingly there are three peaks in the convolution. Lastly, Figure 7(c) shows this procedure for an image representing growth on two di erent grains of a polycrystalline substrate. Again, the histogram shows the di erently aligned groups and the resulting convolution displays a peak for each set of hexagons. In summary, the SEM image tool provides the following functionality: Binary Masking: converts all nonwhite pixels to black Blur: adds Gaussian blur to the image Canny Edge Detector: detects edges in the image Color Mask: mask a portion of the grayscale spectrum Crop: crop the image Dilate: thicken edges in the image Domain Centers: mark centers of graphene domain 22 Figure 7: Edge alignment analyses of arti cially constructed SEM data for (a) a single set of aligned hexagons, (b) three sets of hexagons with di erent orientations and (c) two sets of hexagons with di erent orientations that are located in separate domains. All hexagons have noise added to their angular orientation. The top row shows the original images, whose length scale is arbitrary for this analysis. The edge orientation histogram illustrates the distribution of edges and the convolution plot is the result of the histogram plot convolved with the comb function. The periodic peaks in the orientation histogram demonstrate alignment of the domains. Histograms are normalized. 23 Draw Scale: used to determine the number of pixels per unit length Erase: erases part of the image Erode: thins edges in the image Filter Pattern: masks part of the image using template matching Remove Scale: removes certain types of scales found on SEM images Sobel Filter: applies a Sobel lter to the image Raman Tool Figure 8: Examples of the use of the Raman tool The Raman tool analyzes Raman spectra of graphene, but can potentially be used for other materials as well. Raman spectroscopy is commonly used to check the presence, quality, and the thickness of graphene. Typically, these spectra are analyzed manually, which can be dicult if the spectra are noisy. The Raman tool quantitatively assesses the Raman spectrum 24 of graphene and returns relevant information. A user rst uploads their raw Raman data as a .txt or .csv le. The tool applies a baseline correction by tting the baseline to a quadratic using the method of least squares. It then uses the method of least squares to t each of the three peaks { D,GandG0{ to a Lorentzian function: f(x;x0; ;I) =I 2 (xx0)2+ 2 ; (3) whereIis the peak height, x0is the peak location, and is the half-width at half-maximum. The tool outputs the tted parameters and curves, the number of layers and the quality of graphene (ratio of the peaks in the DandGbands). Figure 8 shows two examples, one of which represents a noisy spectrum. Integration with the Materials Data Facility and DOI Minting The Materials Data Facility (MDF) is a set of data services designed to simplify data pub- lication, automate metadata extraction, and discovery of heterogeneous materials science datasets. Its data publications service enables publication of datasets on distributed storage endpoints, access to the datasets using Globus49,50or standard HTTPs, invocation of custom extraction scripts to collect metadata to promote discoverability (e.g., experimental param- eters, derived analysis results) from published les, and generation of citable digital object identi ers (DOIs). The MDF Discover service serves as a cloud-hosted metadata catalog of the dataset contents and the extracted metadata, enabling researchers to perform complex and granular queries against the published dataset contents (e.g., match all datasets with furnace temperatures between two values or match all datasets published by a given author) and retrieve the matching metadata and dataset contents. These publications and discov- ery capabilities are accessible through both web and programmatic (REST and Python) 25 interfaces allowing integration with the other services needed to build Gr-ResQ. Speci - cally, the Gr-ResQ platform leverages the MDF services to publish data including recipes, spectrographs, electron micrographs, as well as derived analysis results and to catalog the associated parameters and other extracted metadata. Dependency graphs are maintained in the metadata between submitted datasets and derived analyses to make attribution of work clear and easily reportable. In Gr-ResQ, each recipe is represented as an MDF dataset. Datasets are comprised of folders that contain les including the recipe parameters in a custom JSON format, individ- ual images, and raw Raman spectra. Upon publication, custom extractions are performed automatically on the contents of these folders, and the resulting metadata are dispatched to the MDF Discover search index. We append additional data for the Raman analysis, for which a new searchable schema was developed to support meaningful queries of the results. Platform users can use the MDF Forge Python client51to search the repository and nd recipes based on many criteria, including these derived features of the Raman analysis. Re- leases of newly acquired Gr-ResQ data are periodically uploaded to MDF from the Gr-ResQ database. As incentive to users, system administrators collect related recipes into an om- nibus dataset, and once published a DOI is assigned to the new version of the collection of recipes. This DOI minting mechanism enables tracking citations and other metrics related to users' contributions to the database. To do so, Gr-ResQ will periodically mint a DOI associated with all recipes provided by a particular research group, or to a sub-collection of recipes considered to be a \research study" by the group. In this way, the DOIs will reference contributions on a collection basis as opposed to a unique DOI-per-recipe, limiting the cost and tedium that stems from having to mint or cite potentially thousands of DOIs. These group DOIs will be updated as contributing groups add more data to the database. For example, this can be done in preparation for a publication, where new recipes which are part of a speci c research project, can have a single DOI. The current collection of Gr-ResQ datasets in the MDF can be viewed using the service's search page52or using the MDF Forge 26 Python client. Integration with OSCM The Operating System for Cyberphysical Manufacturing (OSCM) is a full-stack operating system to manage manufacturing hardware (machines), manufacturing data (databases), and manufacturing software (applications), in networks of cloud manufacturing.53We have integrated Gr-ResQ with OSCM to enable collaboration among users. Through OSCM, Gr-ResQ users can request speci c experiments from their collaborators within the same research team or across research teams. This platform can enable collaborative experimen- tation and community-wide design of experiments. The OSCM platform is designed to make manufacturing \transactions" automated, safe, and veri able across frictionless and scalable networks. A transaction refers to a manufacturing order that has been placed and exe- cuted by di erent users via their OSCM interactions. OSCM has software components to enable collaborations amongst users, including for machine owners and customers to regis- ter, administer, and access manufacturing capacity in the network through several end-user applications. OSCM integration with Gr-ResQ is one such end-user application. The OSCM client is embedded into the Gr-ResQ tool to allow users to create and interact with trans- actions and experimental resources via the OSCM restful API. In this case, the transaction refers to a request to run a speci c synthesis experiment. The integration between OSCM and Gr-ResQ enables users to reserve time in synthesis labs (facilities) and on equipment (speci c CVD furnaces), as well as to capture all data re- lated to the graphene synthesis experiment in a single transaction. The transaction contains experimental information such as the facility or CVD furnace, the recipe, who created the transaction (customer), who approved and executed the transaction (provider). The trans- action also includes data that is not in the recipe database: live-measured process variables obtained during the experiment such as temperatures, pressures, ows, etc. 27 Figure 9: OSCM Tool work ow. Interaction between customer, provider, transactions, and OSCM. (1) The customer creates a recipe in the OSCM tab inside the Gr-ResQ tool. (2) The OSCM tool sends the transaction to the OSCM cloud and saves it in its database. (3) The provider downloads and executes the recipe. (4) When the experiment is completed, the process data are then attached back to the transaction information located in OSCM Cloud by the provider. (5) The customer downloads the synthesis result. The interaction between Gr-ResQ users and OSCM is depicted in Figure 9. Here the customer is a Gr-ResQ user requesting a speci c synthesis experiment from their colleague or collaborator. This customer creates a recipe in the OSCM tab inside the Gr-ResQ tool. The recipe and additional information about the CVD furnace are automatically encapsulated as a transaction by the OSCM tool when the customer con rms the submission. The OSCM tool sends the transaction to the OSCM cloud and saves it in its database. The provider, who operates the CVD reactor, then downloads the recipe from the transaction in OSCM Cloud to the computer that controls the CVD furnace. The recipe is then imported as an input by the CVD controller. The provider starts the experiment, and while the process is running, an OSCM client embedded in the controller CVD furnace collects and saves the process data (Figure 10). We have developed an application to run a LabView controller of a graphene synthesis CVD reactor. Such applications can greatly accelerate research even within the same laboratory. When the experiment is completed, the process data is then attached to the transaction in OSCM Cloud by the provider. Finally, the customer can pull any transaction information from the OSCM Tool for further analysis. 28 Figure 10: The OSCM client is embedded in the controller software. It collects and saves all process data, which is then attached to a transaction in OSCM Cloud. In short, OSCM integration closes the loop between database users and the experimental facilities generating the data. OSCM allows customers to request synthesis experiments by creating transactions using the OSCM Tool based on a requested recipe. From the provider perspective, OSCM allows an operator to download a recipe job, execute it and upload the result. Both customers and providers are required to be registered OSCM users and can do so via the OSCM Tool or OSCM Cloud. Summary and Future Work We have introduced Gr-ResQ as a powerful platform which includes a synthesis database and a suite of tools to accelerate progress towards manufacturable CVD synthesis of graphene. If adopted by the community, this platform could lead to rapid advancement in graphene synthesis, and later in other 2D materials, and enable the integration of graphene in electronic devices on the industrial scale. We view Gr-ResQ as a holistic approach to meet the objectives of graphene manufacturing de ned as the scalable, sustainable, cost-e ective, and defect-free 29 production of graphene on dielectric substrates for use in future electronics. While basic graphene synthesis has already reached critical milestones, such as large area single domain growth on optimized metal catalyst, the future goals delineated here still greatly challenge the research community. The required advancements could be more e ectively achieved by the community if some of the current hurdles are lifted, such as those associated with nding the most relevant recipes in the vast literature, having sucient data to quickly replicate speci c growth conditions in a di erent lab, and having large amounts of reliable data to enable the use of machine learning and other data-driven tools. Gr-ResQ addresses these needs by providing tools for researchers to organize and analyze their own data as well as data from the broader community, in the form of SEM and Raman spectroscopy post-processing. It also allows users to upload the results of their own recipes into a centralized and standardized table-like database, allowing researchers to learn best practices for the production of high quality graphene. The integration of a recipe database with cyberphysical platforms such as OSCM, allows researchers to seamlessly collaborate in community-driven experimentation and machine learning-based modeling. The functional properties of graphene, including various electric, optical and thermal properties can be added to the database in the future. However, at the current stage, we have not added these properties yet as we need to rst establish a consistent protocol for taking and reporting such measurements. This includes details of transferring the graphene, post-processing, and various details of the measurements. Without these details, the reported properties will be unreliable. We are looking forward to community input regarding the inclusion of measured functional properties. The Gr- ResQ framework is exible and expandable, but we wanted to start from a scope that can be realistically managed at this early stage. Beyond graphene, we believe that a similar approach should be adopted for the synthesis and production of all other 2D materials by CVD,54enabling faster realization of technologies that use these new materials. 30 Supporting Information The Supporting Information le lists all the graphene synthesis parameters collected in the Gr-ResQ database. It describes the database provenance including the synthesis data derived from published