Insightful classification of crystal structures using deep learning

Computational methods that automatically extract knowledge from data are critical for enabling data-driven materials science. A reliable identification of lattice symmetry is a crucial first step for materials characterization and analytics. Current methods require a user-specified threshold, and are unable to detect average symmetries for defective structures. Here, we propose a machine-learning-based approach to automatically classify structures by crystal symmetry. First, we represent crystals by calculating a diffraction image, then construct a deep-learning neural-network model for classification. Our approach is able to correctly classify a dataset comprising more than 100 000 simulated crystal structures, including heavily defective ones. The internal operations of the neural network are unraveled through attentive response maps, demonstrating that it uses the same landmarks a materials scientist would use, although never explicitly instructed to do so. Our study paves the way for crystal-structure recognition of - possibly noisy and incomplete - three-dimensional structural data in big-data materials science.Comment: Nature Communications, in press (2018

Atomic electron tomography in three and four dimensions

Atomic electron tomography (AET) has become a powerful tool for atomic-scale structural characterization in three and four dimensions. It provides the ability to correlate structures and properties of materials at the single-atom level. With recent advances in data acquisition methods, iterative three-dimensional (3D) reconstruction algorithms, and post-processing methods, AET can now determine 3D atomic coordinates and chemical species with sub-Angstrom precision, and reveal their atomic-scale time evolution during dynamical processes. Here, we review the recent experimental and algorithmic developments of AET and highlight several groundbreaking experiments, which include pinpointing the 3D atom positions and chemical order/disorder in technologically relevant materials and capturing how atoms rearrange during early nucleation at four-dimensional atomic resolution

Machine learning for ultrafast X-ray diffraction patterns on large-scale GPU clusters

The classical method of determining the atomic structure of complex molecules by analyzing diffraction patterns is currently undergoing drastic developments. Modern techniques for producing extremely bright and coherent X-ray lasers allow a beam of streaming particles to be intercepted and hit by an ultrashort high energy X-ray beam. Through machine learning methods the data thus collected can be transformed into a three-dimensional volumetric intensity map of the particle itself. The computational complexity associated with this problem is very high such that clusters of data parallel accelerators are required. We have implemented a distributed and highly efficient algorithm for inversion of large collections of diffraction patterns targeting clusters of hundreds of GPUs. With the expected enormous amount of diffraction data to be produced in the foreseeable future, this is the required scale to approach real time processing of data at the beam site. Using both real and synthetic data we look at the scaling properties of the application and discuss the overall computational viability of this exciting and novel imaging technique

Advanced Imaging Techniques for Point-Measurement Analysis of Pharmaceutical Materials

Drugs are an essential element protecting human lives from many diseases such as cancer, diabetes, and cardiovascular disorders. One of the highlights in drug development in recent years is the establishment of rational drug design: a collection of various multi-disciplinary approaches that at the core, focus on designing molecules with specific properties for identified targets and biomolecules with known functional roles and structural information. The candidate molecules will then go through a series of examinations to characterize their physiochemical properties, and an iterative process is used to improve the design of the drug to achieve desirable attributes. The time consuming and highly expensive nature of drug development constantly calls for new analytical techniques that have increasingly higher throughput, faster analysis speed, richer chemical and structural information, and lower risk and cost. Conventional analytical methods for pharmaceutical materials, such as X-ray diffraction analysis and Raman spectroscopy, often suffer from prolonged measurement time. In many cases, the identification of regions of interest within the sample is non-trivial in itself. Nonlinear optical imaging techniques, including second harmonic generation (SHG) microscopy and two-photon excited ultraviolet fluorescence (TPE-UVF) microscopy were developed as fast, real-time, and non-destructive methods for selective identification and characterization of crystalline materials present in pharmaceutical samples. These techniques were integrated with synchrotron X-ray diffraction analysis and Raman spectroscopy to significantly reduce the overall measurement time of these structure characterization techniques. In the meanwhile, with the now increased speed of measurement, the amount of experimental data acquired per unit time has also drastically increased. The rate at which data are analyzed, digested, and interpreted is becoming the bottleneck in data-driving decision-making. Novel electronics that only collect data at the most information-rich time points were employed to significantly increase the signal-to-noise ratio (SNR) during data acquisition, reducing the total amount of data needed for material characterization. Advanced sampling algorithms to reduce the total amount of measurements required for perfect data space reconstruction, automated programs for data acquisition and analysis, and efficient data analysis algorithms based on machine learning were developed for accelerated data processing for nonlinear optical imaging analysis, Raman spectra processing, and X-ray diffraction indexing

PeakNet: Bragg peak finding in X-ray crystallography experiments with U-Net

Serial crystallography at X-ray free electron laser (XFEL) sources has experienced tremendous progress in achieving high data rate in recent times. While this development offers potential to enable novel scientific investigations, such as imaging molecular events at logarithmic timescales, it also poses challenges in regards to real-time data analysis, which involves some degree of data reduction to only save those features or images pertaining to the science on disks. If data reduction is not effective, it could directly result in a substantial increase in facility budgetary requirements, or even hinder the utilization of ultra-high repetition imaging techniques making data analysis unwieldy. Furthermore, an additional challenge involves providing real-time feedback to users derived from real-time data analysis. In the context of serial crystallography, the initial and critical step in real-time data analysis is finding X-ray Bragg peaks from diffraction images. To tackle this challenge, we present PeakNet, a Bragg peak finder that utilizes neural networks and runs about four times faster than Psocake peak finder, while delivering significantly better indexing rates and comparable number of indexed events. We formulated the task of peak finding into a semantic segmentation problem, which is implemented as a classical U-Net architecture. A key advantage of PeakNet is its ability to scale linearly with respect to data volume, making it well-suited for real-time serial crystallography data analysis at high data rates

Multivariate Analysis Applications in X-ray Diffraction

: Multivariate analysis (MA) is becoming a fundamental tool for processing in an efficient way the large amount of data collected in X-ray diffraction experiments. Multi-wedge data collections can increase the data quality in case of tiny protein crystals; in situ or operando setups allow investigating changes on powder samples occurring during repeated fast measurements; pump and probe experiments at X-ray free-electron laser (XFEL) sources supply structural characterization of fast photo-excitation processes. In all these cases, MA can facilitate the extraction of relevant information hidden in data, disclosing the possibility of automatic data processing even in absence of a priori structural knowledge. MA methods recently used in the field of X-ray diffraction are here reviewed and described, giving hints about theoretical background and possible applications. The use of MA in the framework of the modulated enhanced diffraction technique is described in detail

A deep convolutional neural network for real-time full profile analysis of big powder diffraction data

We present Parameter Quantification Network (PQ-Net), a regression deep convolutional neural network providing quantitative analysis of powder X-ray diffraction patterns from multi-phase systems. The network is tested against simulated and experimental datasets of increasing complexity with the last one being an X-ray diffraction computed tomography dataset of a multi-phase Ni-Pd/CeO2-ZrO2/Al2O3 catalytic material system consisting of ca. 20,000 diffraction patterns. It is shown that the network predicts accurate scale factor, lattice parameter and crystallite size maps for all phases, which are comparable to those obtained through full profile analysis using the Rietveld method, also providing a reliable uncertainty measure on the results. The main advantage of PQ-Net is its ability to yield these results orders of magnitude faster showing its potential as a tool for real-time diffraction data analysis during in situ/operando experiments