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Data Science Based Redox Molecule Design for Flow Batteries


EMSL Project ID
51219

Abstract

Data science methods using large data sets from experimental and computational sources are proposed to predict organic redox molecules that will greatly improve redox flow battery performance. Specifically, the aim of the proposed research is to establish a data science-based model to predict aqueous solubility of redox active molecules under various pH conditions by using machine learning techniques based on chemical and physical descriptors for complex structure-property relationships. In addition to experimental descriptors such as aqueous solubilities from the literature, quantum chemistry calculated descriptors will be generated from EMSLs NWChem computational chemistry (i.e., quantum chemistry) suite.

Project Details

Start Date
2019-10-31
End Date
2022-09-30
Status
Closed

Team

Principal Investigator

Amity Andersen
Institution
Environmental Molecular Sciences Laboratory

Team Members

Peiyuan Gao
Institution
Pacific Northwest National Laboratory

Jonathan Sepulveda
Institution
Pacific Northwest National Laboratory

Kaining Duanmu
Institution
Pacific Northwest National Laboratory

Vijayakumar Murugesan
Institution
Pacific Northwest National Laboratory