ProgramMaster Logo
Conference Tools for Materials Science & Technology 2019
Login
Register as a New User
Help
Submit An Abstract
Propose A Symposium
Presenter/Author Tools
Organizer/Editor Tools
About this Abstract
Meeting Materials Science & Technology 2019
Symposium Data Science for Material Property Interpretation
Presentation Title A Machine Learning Approach to Independent Component Analysis for Nuclear Magnetic Resonance Spectra
Author(s) Ryan J. McCarty, Jason Ernest Kahn, David Rotter, Ryan Padilla, Nadia Ahmed, Domingos R. Begalli
On-Site Speaker (Planned) Ryan J. McCarty
Abstract Scope Nuclear magnetic resonance spectroscopy (NMR) is a valuable technique for understanding the atomic structures of solids, but applying NMR to materials containing mixtures of chemical compounds results in complex spectra that are difficult to interpret. Independent component analysis (ICA) techniques allow algorithm-based decompositions of input spectra into statistically relevant components. Ideally, these components should resemble the individual spectra of real chemical compounds, but the quality of the produced components depends on the intuition and technical expertise of the scientist responsible for selecting inputs and preferred algorithms. This process can result in iterative selection until outputs match a preconceived idea of “good” results. To standardize the accuracy of predicted components and variability of individual judgment, we benchmark models constructed using machine learning techniques trained on synthetic NMR spectra to traditional ICA methods to find the best fit components of multi-phase mixtures.
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

Questions about ProgramMaster? Contact programming@programmaster.org