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Meeting 2018 TMS Annual Meeting & Exhibition
Symposium Computational Materials Discovery and Optimization
Presentation Title A Materials-informatics Approach for Finding New Hard-magnetic Phases
Author(s) Johannes J. Möller, Georg Krugel, Wolfgang Körner, Daniel F. Urban, Christian Elsässer
On-Site Speaker (Planned) Johannes J. Möller
Abstract Scope Data-mining and machine-learning (ML) techniques play an increasingly important role in the discovery and development of new materials. In this contribution, we use kernel-based learning methods to predict optimal chemical compositions for new permanent magnets, which are key components in many green-energy technologies. The magnetic-property data used for training and testing the ML models were obtained by a combinatorial high-throughput screening (HTS) using density-functional theory calculations. For encoding the structural and chemical information of the HTS data in a machine-readable format, we use several existing and newly developed material descriptors and assess the predictive power of the ML models built with them. The accuracy of the ML models with an optimal choice of descriptor and model parameters enables the prediction of promising structure-composition combinations for substitutes of state-of-the-art magnetic materials like Nd2Fe14B – with similar intrinsic hard-magnetic properties but less amounts of critical rare-earth elements.
Proceedings Inclusion? Planned: Supplemental Proceedings volume

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A Combined Experimental-computational Approach to Determining Nanoscale Structures
A Materials-informatics Approach for Finding New Hard-magnetic Phases
Computational Screening of Novel Two-dimensional Topological Insulators and Layer-dependent Properties
Data-driven Discovery of Photocathodes for CO2 Reduction
Design Concepts of Optimized MRI Magnet by COMSOL Multiphysics Simulation
Determination of Thermal Transport in Solids and Liquids by Non-equilibrium Molecular Dynamics Simulations
Dual Band Metamaterial Perfect Absorber Based on Mie Resonances
Economic Analysis of National Needs for Technology Infrastructure to Support the Materials Genome Initiative
Fabricating Optimized Crystallographic Textures through Heterogeneous Templated Grain Growth
First-principles Calculations on the Multiferroic Properties of Two-dimensional Oxides
First Principle Prediction of Magnetic Topological Phase in Thin Films of Bi2XY4 (X = Mn, Cr; Y = Se, Te)
High-throughput Investigation of the Electronic Properties of 2D and Bulk Materials in the MaterialsWeb Database
Holistic Computational Structure Screening of More than 12 000 Candidates for Solid Lithium-ion Conductor Materials
Improving the Ductility of Boron Carbide from Computational Design
L-27: Computational Design of Fatigue-resistant NiTi-based Shape Memory Alloys
Learning Grain Boundary Properties from Macroscopic and Microscopic Structural Descriptors
Light-metal Complex Hydrides: Computational Structure Prediction and Interaction with Functionalized Nanoporous Hosts
Machine Learning for Materials
Machine Learning for Prediction of Electronic Structures of Multi-component Alloys
Minimal Addition of Cerium for Stability of Critical Phases in Hard Magnetic AlNiCo Alloys: Combined Machine Learning and CALPHAD
Molecular Crystal Structure Prediction with Gator and Genarris
Predicting Ferroelectric Properties from Microstructures with Deep Learning
Quantum-accurate Force Fields from Machine Learning of Large Materials Data
Reentrant Melting of Sodium, Magnesium and Aluminum and Possible Universal Trend
Search for Rare-Earth Free Permanent Magnets in Fe and Co Based Compounds by Adaptive Genetic Algorithm
Software Tools for High-throughput Materials Data Generation and Data Mining
Structure-property Linkages for Porous Membranes Using the Materials Knowledge Systems Framework
Tailoring Properties in Multi-component Alloys through Heuristic Optimization
The Use of Cluster Expansions to Predict the Structure and Properties of Catalysts

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