| About this Abstract |
| Meeting |
2022 TMS Annual Meeting & Exhibition
|
| Symposium
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ICME Case Studies: Successes and Challenges for Generation, Distribution, and Use of Public/Pre-Existing Materials Datasets
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| Presentation Title |
Generating, Sharing, and Using Halide Perovskite Exploratory Synthesis Data to Discover New Materials |
| Author(s) |
Joshua Schrier |
| On-Site Speaker (Planned) |
Joshua Schrier |
| Abstract Scope |
Over the past four years, we’ve developed a Robotic-Accelerated Perovskite Investigation and Discovery (RAPID) system to make and characterize halide perovskites via inverse temperature crystallization and antisolvent vapor diffusion methods. Simultaneously, we’ve developed a general-purpose open-source data management system— ESCALATE (Experiment Specification, Capture and Laboratory Automation Technology)—which allows humans and algorithms to specify experiments, converts those plans into instructions for human operators and robots, captures collected data and meta-data for reuse, augments those data with cheminformatics and other analyses, and facilitates data export for sharing and machine learning. In this talk, I will describe the RAPID+ESCALATE technology stack and its deployments across multiple laboratories and experiment types. I will then discuss case studies about how we’ve uses this system to enhance data-sharing in publications, improve experimental reproducibility, and discover new scientific insights using the comprehensive data and metadata records. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Machine Learning, Electronic Materials, |