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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
Presentation Title Materials Innovation and Design Enabled by the Materials Project
Author(s) Kristin Aslaug Persson
On-Site Speaker (Planned) Kristin Aslaug Persson
Abstract Scope Fueled by our abilities to compute materials properties and characteristics orders of magnitude faster than they can be measured and recent advancements in harnessing literature data, we are entering the era of the fourth paradigm of science: data-driven materials design. The Materials Project (www.materialsproject.org) contains data derived from quantum mechanical calculations for over 140,000 materials and millions of properties. The resource supports a growing community of data-rich materials research, currently supporting over 200,000 registered users and millions of data records served each day through the API. In this talk, I will highlight some of our ongoing work, as well as success stories of how external users have used Material Project datasets in their work; including a discussion of barriers that users have experienced and what we have done to improve our service in terms of information, accessibility, algorithmic tools and materials data.
Proceedings Inclusion? Planned:
Keywords Computational Materials Science & Engineering, Energy Conversion and Storage, Machine Learning

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

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
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
NOW ON-DEMAND ONLY - Hard Fought Lessons on Open Data and Code Sharing and the Terra Infirma of Ground Truth
The Status of ML Algorithms for Structure-property Relationships Using Matbench as a Test Protocol

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