Part 1: Learning from Data in Material Science.- Designing Novel Multifunctional Materials via Inverse Optimization Techniques.- Quantifying Uncertainties in First Principles Alloy Thermodynamics.- Forward Modeling of Electron Scattering Modalities for Microstructure Quantification.- The Potential of Network Analysis Strategies to HEDM Data: Classification of Microstructures and Prediction of Incipient Failure.- Part 2: Data and Inference.- Challenges of Diagram extraction and Understanding.- Integration of Computational Reasoning, Machine Learning, and Crowdsourcing for Accelerating Materials Discovery.- Computational Creativity for Materials Science.- Optimal Experimental Design Based on Uncertainty Quantification.- Part 3: High-Throughput Calculations and Experiments Functionality-Driven Design and Discovery.- The Use of Proxies and Data for Guiding Materials Synthesis: Examples of Phosphors and Thermoelectrics.- Big Data from Experiments.- Data-Driven Approaches to Combinatorial Materials Science.- Invariant Representations for Robust Materials Prediction.- Part 4: Data Optimization/Challenges in Analysis of Data for Facilities.- The MGI Data Infrastructure.- Is Rigorous Automated Materials Design and Discovery Possible?.- Improve your Monte Carlo: Learn a Control Variate and Correct it with Stacking.- X-ray Free Electron Laser Studies of Shock-Driven Deformation and Phase Transitions.- Coherent Diffraction Imaging Techniques at 3rd and 4th Generation Light Sources.- 3D Data Challenges from X-ray Synchrotron Tomography.- Part 5: Interference/HPC/Software Integration.- Optimal Bayesian Experimental Design: Formulations and New Computational Strategies.- Optimal Bayesian Inference with Missing Data.- Applying an Experimental Design Loop to Shape Memory Alloys.- Big Data Need Big Theory Too.- Combining Experiments, Simulation and Machine Learning in a Single Materials Platform - A Materials Informatics Approach.- Rethinking the HPC Programming Environment. sakriti opis