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Meeting Materials Science & Technology 2019
Symposium Data Science for Material Property Interpretation
Presentation Title Data Analytics for Correlative Multimodal Chemical and Functional Imaging
Author(s) Anton Ievlev, Olga Ovchinnikova
On-Site Speaker (Planned) Anton Ievlev
Abstract Scope Advancing of functional materials requires understanding and control their structure, chemistry and function on the nanoscale. While much of the chemical and structural properties can be studied on macro-scale systems, there is a lack of information about chemical properties on the nanoscale and its correlation to the structure. Here we suggest multimodal chemical and functional imaging approach combining scanning probe microscopy with mass spectrometry and optical spectroscopy to unravel behavior of functional materials. This approach transcends existing techniques by providing nanoscale structural imaging with simultaneous chemical and functional analysis. However, analysis and interpretation of the collected data is complicated by the data multidimensionality and size. Here, I will discuss data analytics techniques for data co-registration and semi-automated interpretation based on multivariate statistical analysis. This research was conducted at the Center for Nanophase Materials Sciences, which is a DOE Office of Science User Facility.
Proceedings Inclusion? Definite: At-meeting proceedings

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

3D Nanoprinting: An Integrated Approach of Experiments, Computer-aided Design and Simulations
4D STEM Data Acquisition, Analytics and Functional Material Property Extraction
A Machine Learning Approach to Independent Component Analysis for Nuclear Magnetic Resonance Spectra
Adversarial Networks for Digital Microstructure Generation
Application of a Statistical Analysis Technique for Characterizing the Deformation Behavior of the Material under Dynamic Impact Loading
Application of Artificial Neural Networks to Low Cycle Fatigue and Creep Data Processing for Power Plant Materials
Automated Defect Detection in Electron Microscopy with Machine Learning
Data Analytics for Correlative Multimodal Chemical and Functional Imaging
Data Science and the MGI
Deciphering the Atomic Origin of Glasses’ Properties by Machine Learning
Deep Learning and MC-X ray, toward Automatic Sample Segmentation
Digital Protocols for Statistical Quantification of Microstructure Features in Polycrystalline Nickel-based Superalloys
Human-in-the-loop Strategies for Dimensionality Reduction and Optimization in Materials Design
Model-based Reconstruction Algorithms for Time-of-Flight Neutron Tomography
Modeling and Simulation of Rare Events in Multidimensional Spaces
Multi-modal Data Fusion and 3D Reconstruction of Serial Sectioning Data
Neural Networks for Processing of Low Signal-to-noise Data in Scanning Probe Microscopy
P2-8: Evaluation for the Quality of Flake Graphite Cast Iron and Spheroidal Graphite Cast Iron by Tapping Test with Using Artificial Intelligence
Phase Field Regularization for Optimal Grain Reconstruction of Noisy Images
Python For Glass Genomics (PyGGi): A Machine Learning Package to Predict the Properties of Glasses
Recent Advances in 3D Reconstruction Based on Spherical Indexing of EBSD Data
Structure Prediction and Property-based Optimization of Molecular Crystals with GAtor
Workflows for Curation and Analysis of Microstructure-Aware Materials Data: Application to Aging of U-Nb Alloys

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