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Meeting 2021 TMS Annual Meeting & Exhibition
Symposium AI/Data informatics: Tools for Accelerated Design of High-temperature Alloys
Sponsorship TMS Materials Processing and Manufacturing Division
TMS: Integrated Computational Materials Engineering Committee
TMS: Computational Materials Science and Engineering Committee
Organizer(s) Michael S. Titus, Purdue University
Pinar Acar, Virginia Tech
Andrew Detor, GE Research
James Edward Saal, Citrine Informatics
Dongwon Shin, Oak Ridge National Laboratory
Scope The fusion of experimental and computational data with artificial intelligence, uncertainty quantification, and theory in Materials Science and Engineering has led to an explosion of research related to the development of new alloy design approaches. While still in its infancy, materials informatics frameworks have been successfully implemented to, for example, predict fatigue life in alloys, identify novel ternary compounds, and design optimized microstructures. The first symposium of this three-year series will focus on Tools. Broadly, Tools in this context refer to experimental, computational, theoretical, and algorithmic developments that enhance fusion between domain knowledge, data, and informatics approaches. New experimental techniques and workflows, novel computational techniques, development of and curation strategies for databases, and novel uses of predictive modeling algorithms – all related to materials informatics frameworks - will be highlighted in this symposium.

Topics of interest include: prediction of mechanical and thermo-physical properties and environmental resistance at elevated temperatures under a variety of conditions (stress, oxidizing/corrosive environments, irradiation). It is expected that the second and third years will focus on case studies and gaps, respectively.

Abstracts Due 07/20/2020
Proceedings Plan Planned:

Advanced Data SCiENce Toolkit for Non-data Scientists (ASCENDS) - A Case Study of the Oxidation Kinetics of NiCr-based Alloys
Bond Energies in Alloys and Oxides as Machine Learning Features
Coupling of Data Mining, Thermodynamics and Multi-objective Genetic Algorithms for the Design of High-temperature Alloys
Curation of Database for High Temperature Materials Using Natural Language Processing Tools
Determining Solute Site Preference and Correlations to Antiphase Boundary Energy in Ni-based Superalloys
Domain and Uncertainty Quantification in Machine Learning Models of Alloy Properties
Domain Knowledge-informed, Process-mapping AI Graph for Designing Fe-based Alloys
Elastic Properties Machine-learning-based Descriptor for a Refractory High Entropy Alloy
Expanding Materials Selection via Transfer Learning for High-temperature Oxide Selection
Exploring the Compositional Space of High Entropy Alloys via Sequential Learning
Knowledge-driven Platform for Federated Multimodal Big Data Storage & Analytics
Leveraging eXtremeMAT’s Data-driven Framework for Prediction of Creep Life for High-temperature Alloys
Machine Learning of Phase Formation of CCAs
Optimal Design of High-temperature, Oxidation-resistant Complex Concentrated Alloys
Predicting Thermophysical Properties of Metallic Alloys Using Machine Learning
Predicting Vibrational Entropy of FCC Solids Uniquely from Bond Chemistry Using Machine Learning
Predicting Yield Stress of High Temperature Alloys via Computer Vision and Machine Learning
Revealing Nanoscale Features Controlling Diffusion Within Multi-component Alloys through Machine Learning
Toward High Throughput Design and Development of Multi-principal Element Alloys for Corrosion and Oxidation Resistance (MPEAs)
Uncertainty Quantification for Thermo-mechanical Behavior of Aircraft Engine Materials in Elevated Temperatures
Uncertainty Reduction for Calculated Phase Equilibria

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