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Meeting 2022 TMS Annual Meeting & Exhibition
Symposium ICME Case Studies: Successes and Challenges for Generation, Distribution, and Use of Public/Pre-Existing Materials Datasets
Sponsorship TMS Materials Processing and Manufacturing Division
TMS: Integrated Computational Materials Engineering Committee
Organizer(s) Stephen J. DeWitt, Oak Ridge National Laboratory
Vikas Tomar, Purdue University
James Edward Saal, Citrine Informatics
James A. Warren, National Institute of Standards and Technology
Scope The emergence of digital data, public data repositories, and machine learning enables a new paradigm of materials research where high-quality datasets can be published and then reused and reanalyzed by other research teams, perhaps enabling entirely different applications than originally intended. The release of publicly available datasets has accelerated in recent years, encompassing varied datatypes such as densely sampled experimental data (e.g., synchrotron spectra and 3D serial section reconstructions), large quantities of image data (e.g, microstructure micrograph libraries), literature reviews containing sparsely populated and diversely measured material properties, and high-throughput large-scale simulation databases. The availability of these datasets provides the potential for faster and more cost-effective materials research by reducing unnecessary duplication of effort and effective division of labor. Despite these opportunities, this mode of research faces several challenges, including insufficient or incorrectly recorded metadata, lean or biased sampling of the materials space limiting (re-)analysis, and cultural norms limiting data sharing and accessibility.

This symposium solicits abstract submissions from researchers who are engaging in this research paradigm to share their experiences of the opportunities and challenges. Research involving dataset creation and publication and research involving reuse/reanalysis of external datasets are equally of interest. Relevant topics include, but are not limited to:

• Case studies reviewing the successes and challenges of providing and/or using public datasets
• The provision of adequate metadata for reuse, or the use of datasets in the face of limited metadata
• Utilizing lean datasets for model building when further data acquisition is not possible
• Merging disparate datasets into a single cohesive dataset
• Model validation using externally obtained, high-dimensional digital datasets
• Examples of large dataset quality assessment, cleaning, and curation
• Uncertainty quantification of ICME predictions from lean data
• The public release of machine learning models trained on proprietary data such that the propriety data is protected

Abstracts Due 07/19/2021
Proceedings Plan Planned:
PRESENTATIONS APPROVED FOR THIS SYMPOSIUM INCLUDE

A Quest for Re-using 3D Materials Data
A validation framework for microstructure-sensitive fatigue simulation models
Added value and increased organization: Capturing experimental data provenance in Materials Commons 2.0
Challenges in Producing, Curating, and Sharing Large Multimodal, Multi-institutional Data Sets for Additive Manufacturing
Data-driven model based comparison of public datasets for online State of Charge Estimation in Lithium-ion Batteries
Filling Data Gaps in 3D Microstructure with Deep Learning
Generating, Sharing, and Using Halide Perovskite Exploratory Synthesis Data to Discover New Materials
Graph convolutional neural networks for fast, accurate prediction of material properties for solid solution high entropy alloys using open-source datasets
Hard Fought Lessons on Open Data and Code Sharing and the Terra Infirma of Ground Truth
Holistic merging of experimental and computational datasets – a case study for diffusion coefficients
Materials Innovation and Design enabled by the Materials Project
Mg Database Project: Mapping Trends and Data Sets of Magnesium and Its Alloys for Improved Mechanical Performance
The status of ML algorithms for structure-property relationships using Matbench as a test protocol
Thermodynamic-based precipitation modelling as a tool in steel development: Model implementation and case studies


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