Investment in brighter sources and larger and faster detectors has accelerated the speed of data acquisition at national user facilities. The accelerated data acquisition offers many opportunities for discovery of new materials, but it also presents a daunting challenge. The rate of data acquisition far exceeds the current speed of data quality assessment, resulting in less than optimal data and data coverage, which in extreme cases forces recollection of data. Herein, we show how this challenge can be addressed through development of an approach that makes routine data assessment automatic and instantaneous. Through extracting and visualizing customized attributes in real time, data quality and coverage, as well as other scientifically relevant information contained in large datasets is highlighted. Deployment of such an approach not only improves the quality of data but also helps optimize usage of expensive characterization resources by prioritizing measurements of highest scientific impact. We anticipate our approach to become a starting point for a sophisticated decision-tree that optimizes data quality and maximizes scientific content in real time through automation. With these efforts to integrate more automation in data collection and analysis, we can truly take advantage of the accelerating speed of data acquisition.
Investment in brighter sources and larger detectors has resulted in an explosive rise in the data collected at synchrotron facilities. Currently, human experts extract scientific information from these data, but they cannot keep pace with the rate of data collection. Here, we present three on-the-fly approaches - attribute extraction, nearest-neighbor distance, and cluster analysis - to quickly segment x-ray diffraction (XRD) data into groups with similar XRD profiles. An expert can then analyze representative spectra from each group in detail with much reduced time, but without loss of scient…
Rust-accelerated powder X-ray diffraction simulation for high-throughput and machine-learning-driven materials science
Faculty of Mechanical Engineering, Czech Technical University in Prague（布拉格捷克理工大学机械工程学院）；Faculty of Nuclear Sciences and Physical Engineering, Czech Technical University in Prague（布拉格捷克理工大学核科学与物理工程学院）；FZU – Institute of Physics of the Czech Academy of Sciences（捷克科学院物理研究所）
MatDiffract: A Material-Informed Automated Analysis Platform for X-ray Powder Diffraction
Institute of High Energy Physics, Chinese Academy of Sciences（中国科学院高能物理研究所）；Spallation Neutron Source Science Center（散裂中子源科学中心）；College of Computer and Information Engineering, Henan Normal University（河南师范大学计算机与信息工程学院）；Dongguan Institute of Materials Science and Technology, Chinese Academy of Sciences（中国科学院东莞材料科学与技术研究所）；Songshan Lake Material Laboratory（松山湖材料实验室）；School of Computer Science, Sun Yat-sen University（中山大学计算机学院）
Orchestrating parametric fitting of multicomponent spectra at scale is an essential yet underappreciated task in high-throughput quantification of materials and chemical composition. To automate the annotation process for spectroscopic and diffraction data collected in counts of hundreds to thousands, we present a systematic approach compatible with high-performance computing infrastructures using the MapReduce model and task-based parallelization. We implement the approach in software and demonstrate linear computational scaling with respect to spectral components using multidimensional exper…
We develop a computational workflow for high-throughput Wannierization of density functional theory (DFT) based electronic band structure calculations. We apply this workflow to 1771 materials, and we create a database with the resulting Wannier-function based tight binding Hamiltonians (WTBH). We evaluate the accuracy of the WTBHs by comparing the Wannier band structures to directly calculated DFT band structures on both the set of k-points used in the Wannierization as well as independent k-points from high symmetry lines. Accurate WTBH can be used for the calculation of many materials prope…
We apply variational autoencoders (VAE) to X-ray diffraction (XRD) data analysis on both simulated and experimental thin-film data. We show that crystal structure representations learned by a VAE reveal latent information, such as the structural similarity of textured diffraction patterns. While other artificial intelligence (AI) agents are effective at classifying XRD data into known phases, a similarly conditioned VAE is uniquely effective at knowing what it does not know, rapidly identifying novel phases and mixtures. These capabilities demonstrate that a VAE is a valuable AI agent for mate…
Based on a set of machine learning predictions of glass formation in the Ni-Ti-Al system, we have undertaken a high-throughput experimental study of that system. We utilized rapid synthesis followed by high-throughput structural and electrochemical characterization. Using this dual-modality approach, we are able to better classify the amorphous portion of the library, which we found to be the portion with a full-width-half-maximum (FWHM) of 0.42 A$^{-1}$ for the first sharp x-ray diffraction peak. We demonstrate that the FWHM and corrosion resistance are correlated but that, while chemistry st…
Compositionally graded Fe-Pt thin films were prepared on stationary 100 mm Si substrates by magnetron sputtering a base target of Fe on which a piece of Pt is asymmetrically positioned. Energy Dispersive X-Ray analysis was used to map the variation in film composition across the substrate, as a function of the size of the Pt piece. A scanning polar Magneto-Optical-Kerr-Effect system was used to probe the influence of composition and post-deposition annealing conditions (temperature and time) on coercivity. In this way the maximum coercivity achievable for the sputtering system used could be es…
Development in lattice strain mapping using four-dimensional scanning transmission electron microscopy (4D-STEM) method now offers improved precision and feasibility. However, automatic and accurate diffraction analysis is still challenging due to noise and the complexity of intensity in diffraction patterns. In this work, we demonstrate an approach, employing the blob detection on cross-correlated diffraction patterns followed by lattice fitting algorithm, to automate the processing of four-dimensional data, including identifying and locating disks, and extracting local lattice parameters wit…
High-throughput synthesis of thermoelectric Ca$_3$Co$_4$O$_9$ films
复合氧化物薄膜的性质可以在作为失配、组成、取向和结构的函数的值的范围内调节。在这里，我们报告了一种策略，用于生长结构化的外延热电薄膜，从而提高塞贝克系数。代替使用单晶蓝宝石衬底来支持外延生长，使用脉冲激光沉积技术将Ca $_3$ Co $_3$ O $_9$膜沉积到通过火花等离子体烧结纹理化的Al $_2$ O $_3$多晶衬底上。的2000纳米薄膜的结构质量进行了研究，通过透射电子显微镜，而晶粒的晶体取向和外延的关系，由电子背散射衍射确定。多晶陶瓷模板的使用导致结构化的膜，是在良好的局部外延注册。塞贝克系数在300 K时约为170 $μ$ V/K，这是具有低载流子密度的失配材料的典型值。这种高通量的过程，称为组合衬底外延，似乎有利于合理调整功能氧化膜，开辟了一条路线，高品质的复合氧化物的外延合成。
Twenty-one distinct AlxCoCrFeNi alloys were rapidly prepared by laser alloying an equiatomic CoCrFeNi substrate with Al powder to create an alloy library ranging x=0.15-1.32. Variations in crystal structure, microstructure and mechanical properties were investigated using X-ray diffraction, scanning electron microscopy, scanning transmission electron microscopy and nanoindentation. With increasing Al content, the crystal structure transitioned from a disordered FCC to a mixture of disordered BCC and ordered B2 structures. While the onset of BCC/B2 formation was consistent with previously repor…
A high-throughput experimental approach is presented to extract the anisotropic interdiffusion coefficient by combining information over the composition profiles obtained by the electron probe microanalysis (EPMA) and the grain orientation spectrum by the electron backscatter diffraction (EBSD) on polycrystalline diffusion couples. Following the forward-simulation scheme, the interdiffusion coefficients in grains with diverse orientation are obtained and subsequently used to determine the anisotropic interdiffusion coefficients perpendicular (D_\perp (x)) and parallel (D_\parallel (x)) to the …
Coherent Diffraction Imaging (CDI), a technique where an object is reconstructed from a single (2D or 3D) diffraction pattern, recovers the lost diffraction phases without a priori knowledge of the extent (support) of the object, which prevents an unambiguous metric evaluation of solutions. We propose to use a 'free' log-likelihood indicator, where a small percentage of points are masked from the reconstruction algorithms, as an unbiased metric to evaluate the validity of proposed solutions, independent of the sample studied. We also show how a set of solutions can be analysed through an eigen…
The discovery of new structural and functional materials is driven by phase identification, often using X-ray diffraction (XRD). Automation has accelerated the rate of XRD measurements, greatly outpacing XRD analysis techniques that remain manual, time-consuming, error-prone, and impossible to scale. With the advent of autonomous robotic scientists or self-driving labs, contemporary techniques prohibit the integration of XRD. Here, we describe a computer program for the autonomous characterization of XRD data, driven by artificial intelligence (AI), for the discovery of new materials. Starting…
Hierarchical Unsupervised Clustering of Nanobeam Electron Diffraction Data Using Sparse Bragg-Vector Representations
University of Glasgow（格拉斯哥大学）；Optimum Instrument Ltd（Optimum Instrument有限公司）；Helmholtz-Zentrum Dresden-Rossendorf（亥姆霍兹德累斯顿罗森多夫研究中心）；University of Oxford（牛津大学）；University of Sydney（悉尼大学）
Max-Planck-Institut für Eisenforschung GmbH（马克斯·普朗克钢铁研究所）；The University of Alabama（阿拉巴马大学）；Imperial College London（伦敦帝国理工学院）；The University of Sydney（悉尼大学）
Volumetric crystal structure indexing and orientation mapping are key data processing steps for virtually any quantitative study of spatial correlations between the local chemistry and the microstructure of a material. For electron and X-ray diffraction methods it is possible to develop indexing tools which compare measured and analytically computed patterns to decode the structure and relative orientation within local regions of interest. Consequently, a number of numerically efficient and automated software tools exist to solve the above characterisation tasks. For atom probe tomography (APT…
KAN-Enhanced Contrastive Learning Accelerating Crystal Structure Identification from XRD Patterns
Materials Genome Institute, Shanghai University（上海大学材料基因组研究所）；The University of Sydney（悉尼大学）；Shanghai University（上海大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
Analyzing large X-ray diffraction (XRD) datasets is a key step in high-throughput mapping of the compositional phase diagrams of combinatorial materials libraries. Optimizing and automating this task can help accelerate the process of discovery of materials with novel and desirable properties. Here, we report a new method for pattern analysis and phase extraction of XRD datasets. The method expands the Nonnegative Matrix Factorization method, which has been used previously to analyze such datasets, by combining it with custom clustering and cross-correlation algorithms. This new method is capa…
High-throughput experiments that use combinatorial samples with rapid measurements can be used to provide process-structure-property information at reduced time, cost, and effort. Developing these tools and methods is essential in additive manufacturing where new process-structure-property information is required on a frequent basis as advances are made in feedstock materials, additive machines, and post-processing. Here we demonstrate the design and use of combinatorial samples produced on a commercial laser powder bed fusion system to study 60 distinct process conditions of nickel superalloy…
High-throughput validation of phase formability and simulation accuracy of Cantor alloys
University of Toronto（多伦多大学）；King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）；Idaho National Laboratory（爱达荷国家实验室）；Rowan University（罗文大学）；IBM T.J. Watson Research Center（IBM托马斯·J·沃森研究中心）；Canadian Light Source（加拿大光源）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；Schwartz Reisman Institute for Technology and Society（施瓦茨·赖斯曼技术与社会研究所）；Vector Institute for Artificial Intelligence（向量人工智能研究院）
AI-Driven Structure Refinement of X-ray Diffraction
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Shanghai University（上海大学）；University of Science and Technology of China（中国科学技术大学）
The freud Python package is a powerful library for analyzing simulation data. Written with modern simulation and data analysis workflows in mind, freud provides a Python interface to fast, parallelized C++ routines that run efficiently on laptops, workstations, and supercomputing clusters. The package provides the core tools for finding particle neighbors in periodic systems, and offers a uniform API to a wide variety of methods implemented using these tools. As such, freud users can access standard methods such as the radial distribution function as well as newer, more specialized methods suc…
High-throughput grain mapping with sub-nanometer spatial resolution is demonstrated using scanning nanobeam electron diffraction (also known as 4D scanning transmission electron microscopy, or 4D-STEM) combined with high-speed direct electron detection. An electron probe size down to 0.5 nm in diameter is implemented and the sample investigated is a gold-palladium nanoparticle catalyst. Computational analysis of the 4D-STEM data sets is performed using a disk registration algorithm to identify the diffraction peaks followed by feature learning to map the individual grains. Two unsupervised fea…
Accelerating the experimental cycle for new materials development is vital for addressing the grand energy challenges of the 21st century. We fabricate and characterize 75 unique halide perovskite-inspired solution-based thin-film materials within a two-month period, with 87% exhibiting band gaps between 1.2 eV and 2.4 eV that are of interest for energy-harvesting applications. This increased throughput is enabled by streamlining experimental workflows, developing a set of precursors amenable to high-throughput synthesis, and developing machine-learning assisted diagnosis. We utilize a deep ne…
opXRD: Open Experimental Powder X-ray Diffraction Database
Karlsruhe Institute of Technology (KIT)（卡尔斯鲁厄理工学院）；Hong Kong University of Science and Technology (Guangzhou) (HKUST)（香港科技大学（广州））；University of Southern California (USC)（南加州大学）；Lawrence Berkeley National Laboratory (LBNL)（劳伦斯伯克利国家实验室）；Shenzhen Polytechnic（深圳职业技术大学）；King Fahd University of Petroleum and Minerals (KFUPM)（法赫德国王石油矿产大学）；Chimie ParisTech（巴黎化学学院）；PSL University（巴黎文理大学）；CNRS（法国国家科学研究中心）；Institut de Recherche de Chimie Paris（巴黎化学研究所）；Empa–Swiss Federal Laboratories for Materials Science and Technology (EMPA)（瑞士联邦材料科学与技术实验室）；Forschungszentrum Juelich GmbH（于利希研究中心）；Engler-Bunte-Institut & Institute of Catalysis Research and Technology（恩格勒-本生研究所及催化研究与技术研究所）
Constrained non-negative matrix factorization enabling real-time insights of $\textit{in situ}$ and high-throughput experiments
National Synchrotron Light Source II, Brookhaven National Laboratory（布鲁克海文国家实验室国家同步辐射光源II）；Center for Computational Biology, Flatiron Institute（弗拉蒂隆研究所计算生物学中心）
