Abstract
The shift of energy production towards renewable, yet at times inconsistent, resources like solar and wind have increased the need for better energy storage solutions. An emerging energy storage technology that is highly scalable and cost-effective is the redox flow battery comprised of redox-active organic materials. Designing optimum materials for redox flow batteries involves balancing key properties such as the redox potential, stability, and solubility of the redox-active molecules. Here, we present the data-enabled discovery and design to transform liquid-based energy storage (D3TaLES) database, a curated data collection of more than 43 000 redox-active organic molecules that are of potential interest as the redox-active species for redox flow batteries with the aim to offer readily accessible and uniform data for big data metanalyses. D3TaLES raw data and derived properties are organized into a molecule-centric schema, and the database ontology contributes to the establishment of community reporting standards for electrochemical data. Data are readily accessed and analyzed through an easy-to-use web interface. The data infrastructure is coupled with data upload and processing tools that extract, transform, and load relevant data from raw computation or experimental data files, all of which are available to the public via a D3TaLES API. These processing tools along with an embedded high-throughput computational workflow enable community contributions and versatile data sharing and analyses, not only in redox-flow battery research but also in any field that applies redox-active organic molecules.
| Original language | English |
|---|---|
| Pages (from-to) | 1152-1162 |
| Number of pages | 11 |
| Journal | Digital Discovery |
| Volume | 2 |
| Issue number | 4 |
| DOIs | |
| State | Published - Aug 1 2023 |
Bibliographical note
Publisher Copyright:© 2023 The Author(s). Published by the Royal Society of Chemistry.
Funding
This work was generously supported by the National Science Foundation (NSF) under Cooperative Agreement Number 2019574. Computational resources were provided through an NSF Extreme Science and Engineering Discovery Environment (XSEDE) Resource Allocation Award (CHE200119) and Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support (ACCESS) DISCOVER Allocation Award (PHY220121). We further acknowledge the University of Kentucky (UK) Center for Computational Sciences and Information Technology Services Research Computing for their fantastic support and collaboration, and use of the Lipscomb Compute Cluster and associated research computing resources. Finally, we wholeheartedly thank the entire D3TaLES team for their insights into the development of this data architecture.
| Funders | Funder number |
|---|---|
| NSF Extreme Science and Engineering Discovery Environment | |
| XSEDE | PHY220121, CHE200119 |
| National Science Foundation Arctic Social Science Program | 2019574 |
| University of Kentucky |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
ASJC Scopus subject areas
- Chemistry (miscellaneous)
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