ProgramMaster Logo
Conference Tools for 2019 TMS Annual Meeting & Exhibition
Login
Register as a New User
Help
Submit An Abstract
Propose A Symposium
Presenter/Author Tools
Organizer/Editor Tools
About this Abstract
Meeting 2019 TMS Annual Meeting & Exhibition
Symposium Computational Approaches for Big Data, Artificial Intelligence and Uncertainty Quantification in Computational Materials Science
Presentation Title Automatminer: An Automatic Materials Science Machine Learning Tool for Benchmarking and Prediction
Author(s) Alexander Russell Dunn, Alireza Faghaninia, Qi Wang, Alex Ganose, Anubhav Jain
On-Site Speaker (Planned) Alexander Russell Dunn
Abstract Scope Automatminer is software designed to help material scientists benchmark machine learning algorithms when predicting a target property. Powered by matminer (https://hackingmaterials.github.io/matminer/), a Python-based data mining package, automatminer generates features from minimal input data such as chemical formula and/or crystal structure. It then automatically trains and evaluates several machine learning algorithms to predict a target property, benchmarking each model based on user-defined metrics. Models are generated without user intervention, and thus can be used as a first-order benchmark for more complex hand-tuned machine learning models. We present results of automatminer property prediction for thermodynamic stability, metallic glass formation, defect formation energies, and dielectric and elastic properties and compare them against published machine learning models.
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

Questions about ProgramMaster? Contact programming@programmaster.org