Materials Discovery and Design By Means of Data Science and Optimal Learning /

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Lookman, Turab (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Eidenbenz, Stephan (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Alexander, Frank (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Barnes, Cris (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2018.
• Έκδοση: 1st ed. 2018.
• Σειρά: Springer Series in Materials Science, 280
• Θέματα: Physics.; Materials science.; Data mining.; Engineering-Materials.; Computer mathematics.; Numerical analysis.; Numerical and Computational Physics, Simulation.; Characterization and Evaluation of Materials.; Data Mining and Knowledge Discovery.; Materials Engineering.; Computational Science and Engineering.; Numerical Analysis.

This book addresses the current status, challenges and future directions of data-driven materials discovery and design. It presents the analysis and learning from data as a key theme in many science and cyber related applications. The challenging open questions as well as future directions in the ap...

Πίνακας περιεχομένων:

• 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.