Del i serien Springer Series in Materials Science

Materials Discovery and Design

By Means of Data Science and Optimal Learning

Häftad, Engelska, 2019

AvTurab Lookman,Stephan Eidenbenz,Frank Alexander,Cris Barnes

2 389 kr

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Thus, the importance of this book lies in emphasizing that the full value of knowledge driven discovery using data can only be realized by integrating statistical and information sciences with materials science, which is increasingly dependent on high throughput and large scale computational and experimental data gathering efforts.

Produktinformation

Utgivningsdatum2019-01-19
Mått155 x 235 x undefined mm
FormatHäftad
SpråkEngelska
SerieSpringer Series in Materials Science
Antal sidor256
FörlagSpringer Nature Switzerland AG
ISBN9783030076023

Tillhör följande kategorier

Matematisk fysik inom Naturvetenskap och teknik
Systemvetenskap och AI inom Data och IT
Maskinteknik och material inom Naturvetenskap och teknik
Databaser inom Data och IT

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.