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Meeting 2019 TMS Annual Meeting & Exhibition
Symposium Computational Approaches for Big Data, Artificial Intelligence and Uncertainty Quantification in Computational Materials Science
Presentation Title Materials Platform for Data Science: From Big Data towards Materials Genome
Author(s) Evgeny Blokhin, Pierre Villars
On-Site Speaker (Planned) Evgeny Blokhin
Abstract Scope Since 1993 the PAULING FILE project team led by Pierre Villars (Switzerland) extracts materials data from the peer-reviewed literature in materials science. As of 2018, nearly 300k publications have been processed, and about 400k crystal structures, 60k phase diagrams, and 1M other property sets were extracted. All data have been systematized and critically evaluated. Today PAULING FILE backs up such commercial products as Springer Materials and MedeA Materials Design. The Materials Platform for Data Science (MPDS, www.mpds.io) is an online edition of the PAULING FILE, providing the data in machine-readable format via the REST API. Using the PAULING FILE data, we present a holistic vision for all the inorganic substances. Coupled with the high-throughput ab initio simulations and the top-down/bottom-up artificial intelligence techniques, our vision provides a key to the discovery of materials genome, playing a role of the periodic table for the entire set of materials.
Proceedings Inclusion? Planned: Supplemental Proceedings volume

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A Machine Learning Exploration of Grain Boundary Mobility Mechanisms
A Machine Learning Framework to Improve nanoHUB Prediction Capabilities Using Existing Tool Data
A Reification Approach to Modeling Material Response by Fitting Johnson Cook Parameters
Accelerating Discovery of Compositionally Complex Amorphous Structural Alloys
Addressing Uncertainty Associated with Classical Interatomic Potential Choice
An Autonomous Characterization System for Limited-data Experimental Materials Screening: Composition Spread Thin Film Experiments
Application of Natural Language Processing to TMS Abstracts to Understand the Direction of Computational Materials Design
Applying Machine Learning Techniques to Predict Precipitate Morphology for Alloy Design and Uncertainty Quantification
Automated Sensitivity Analysis for High-throughput Ab Initio Calculations
Automatminer: An Automatic Materials Science Machine Learning Tool for Benchmarking and Prediction
Bayesian CALPHAD: From Uncertainty Quantification to Model Fusion
Characterizing the Likelihood of Success of Using Machine Learning to Design Novel Materials
Cloud-based Infrastructure for Big Data in the Materials Domain
Cloud-based Surrogate Models for Composite Materials
Comprehensive Quality Assurance of Additive Manufacturing Ti-6Al-4V by Learning from Prior Studies
Developing Fast-running Simulations Models for Manufacturing Using Deep Learning
Efficient Propagation of Uncertainty From CALPHAD to Multi-physics Phase Field Microstructure Simulations
Error Estimation for Stress Distributions and Macroscale Yield Prediction in Polycrystalline Alloys
Evaluation and Representation of Uncertainty in Thermodynamic Phase Diagrams
GB Property Localization: Inference and Uncertainty Quantification of GB Structure-property Models from Indirect Polycrystal Measurements
High-performance Computing in Artificial Neural Networks Atomistic Simulations
Impact of Uncertainty Quantification in Automated CALPHAD Modeling on the Design of Additively Manufactured Functionally-graded Alloys
Investigation of Deformation Twinning in Mg Alloy during In-situ Compression Using Clustering and Computer Vision
M-10: Thermocouple Temperature Measurement and Thermal Modelling of Zircaloy-4 during Electron Beam Welding
M-5: Efficacy of a Mathematical Model in Mimicking Trabecular Bone Structures Using Deep Learning Techniques
M-6: Material Parameter Estimation for Phase-field Model of Binary Alloy Solidification Using EnKF-based Data Assimilation
M-8: Prediction of Biaxial Tensile Deformation Behavior of Aluminum Alloy Using Crystal Plasticity Finite Element Method and Machine Learning
M-9: Sequential Experiments Design for Acceleration the Developments of NiTi-based Shape Memory Alloys
Machine-learning-aided Design of Metallic Glasses
Machine Learning for High-Temperature Alloy Design: High-Quality Data, Scientific Descriptors and Curve Fitting
Machine Learning of Materials Synthesis by Data Extraction from over 3 Million Research Papers
Machine Learning to Predict Continuous Cooling Phase Transformations in Steels
Machine Learning with Force-field Inspired Descriptors for Materials: Fast Screening and Mapping Energy Landscape
Materials Informatics and Big Data: Realization of 4th Paradigm of Science in Materials Science
Materials Platform for Data Science: From Big Data towards Materials Genome
Materials Science Learning and Discovery from Large-scale Text Mining
Max Phase Thermo-mechanical Approximation via Machine Learning
Modeling Complex Phenomena in 2D Materials Using First-principles Theory Based Machine Learning Force Fields
Optimization of Calibration Methods for a Reduced-order Structure Property Linkage of Polycrystalline Materials
Perspectives on the Impact of Machine Learning, Deep Learning, and Artificial Intelligence on Materials, Processes, and Structures Engineering
Polymer Genome: An Informatics Platform for Rational Polymer Dielectrics Design and Beyond
Quantifying Uncertainty in High Strain Rate Materials Strength with Bayesian Inference
Reduced Order Crystal Plasticity Modelling for ICME Using a Machine Learning Approach
Research Progress in Machine Learning Building Layered Material Model and Predicting Thermoelectric Performance
Software Tools, Crystal Descriptors, and Applications of Machine Learning Applied to Materials Design
Steel Inclusion Classification Using Computer Vision and Machine Learning
Towards Predictive Synthesis of Computer-designed Inorganic Materials
Uncertainty Quantification in Microstructural Reconstruction of Additively Manufactured Materials
Uncertainty Quantification in Solidification Modeling of Additive Manufacturing
Unsupervised Segmentation of Microstructures
Workflow for High-throughput Atomistic Models of Ceramic Interfaces

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