Symmetry prediction and knowledge discovery from X-ray diffraction patterns using an interpretable machine learning approach

Automated estimation of materials parameter from X-ray absorption and electron energy-loss spectra with similarity measures

(2019) Yuta Suzuki et al.   npj Computational Materials

Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks

(2019) Felipe Oviedo et al.   npj Computational Materials

Discovery of new materials using combinatorial synthesis and high-throughput characterization of thin-film materials libraries combined with computational methods

(2019) Alfred Ludwig   npj Computational Materials

Crystallographic prediction from diffraction and chemistry data for higher throughput classification using machine learning

(2019) Jeffery A. Aguiar et al.   COMPUTATIONAL MATERIALS SCIENCE

Decoding crystallography from high-resolution electron imaging and diffraction datasets with deep learning

(2019) J. A. Aguiar et al.   Science Advances

Crystal Structure Prediction via Deep Learning

(2018) Kevin Ryan et al.   JOURNAL OF THE AMERICAN CHEMICAL SOCIETY

Insightful classification of crystal structures using deep learning

(2018) Angelo Ziletti et al.   Nature Communications

A convolutional neural network-based screening tool for X-ray serial crystallography

(2018) Tsung-Wei Ke et al.   JOURNAL OF SYNCHROTRON RADIATION

Adaptive design of an X-ray magnetic circular dichroism spectroscopy experiment with Gaussian process modelling

(2018) Tetsuro Ueno et al.   npj Computational Materials

Machine learning for molecular and materials science

(2018) Keith T. Butler et al.   NATURE

Unsupervised phase mapping of X-ray diffraction data by nonnegative matrix factorization integrated with custom clustering

(2018) Valentin Stanev et al.   npj Computational Materials

Inferring phase diagrams from X-ray data with background signals using graph segmentation

(2017) Shaobo Li et al.   MATERIALS SCIENCE AND TECHNOLOGY

Classification of crystal structure using a convolutional neural network

(2017) Woon Bae Park et al.   IUCrJ

Comparison of dissimilarity measures for cluster analysis of X-ray diffraction data from combinatorial libraries

(2017) Yuma Iwasaki et al.   npj Computational Materials

Semi-Supervised Approach to Phase Identification from Combinatorial Sample Diffraction Patterns

(2016) Jonathan Kenneth Bunn et al.   JOM

Using Similarity Metrics to Quantify Differences in High-Throughput Data Sets: Application to X-ray Diffraction Patterns

(2016) Efraín Hernández-Rivera et al.   ACS Combinatorial Science

Automated Phase Mapping with AgileFD and its Application to Light Absorber Discovery in the V–Mn–Nb Oxide System

(2016) Santosh K. Suram et al.   ACS Combinatorial Science

ImageNet Large Scale Visual Recognition Challenge

(2015) Olga Russakovsky et al.   INTERNATIONAL JOURNAL OF COMPUTER VISION

Generalized machine learning technique for automatic phase attribution in time variant high-throughput experimental studies

(2015) Jonathan Kenneth Bunn et al.   JOURNAL OF MATERIALS RESEARCH

High-throughput determination of structural phase diagram and constituent phases using GRENDEL

(2015) A G Kusne et al.   NANOTECHNOLOGY

High-throughput synchrotron X-ray diffraction for combinatorial phase mapping

(2014) J. M. Gregoire et al.   JOURNAL OF SYNCHROTRON RADIATION

Design of a Full-Profile-Matching Solution for High-Throughput Analysis of Multiphase Samples Through Powder X-ray Diffraction

(2009) Laurent A. Baumes et al.   CHEMISTRY-A EUROPEAN JOURNAL

Rapid identification of structural phases in combinatorial thin-film libraries using x-ray diffraction and non-negative matrix factorization

(2009) C. J. Long et al.   REVIEW OF SCIENTIFIC INSTRUMENTS