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Aqueous electrolytes

2020-12-23

Overview

The following dataset comprises all aqueous electrolytes characterized as part of Dave, 2020. The electrolytes described contain survey data from various lithium and sodium salts in water up to their solubility limits, including sulfates, nitrates, perchlorates, and bromides. In addition to surveys, a machine-learning guided sampling of multi-salt mixtures is provided. The aqueous electrolyte dataset is provided in both .json and .pkl (protocol 3) format. Once loaded, the dataset is a list of 251 dictionaries with the following keys:

  1. GUID: a unique identifier for each of the 251 experiments available
  2. conductivity: specific conductivity of the electrolyte (as measured by 4-electrode Consort C3410)
  3. temperature: temperature at which conductivity is measured (also provided by Consort above)
  4. RUN_ID: a unique identifier for the specific Python script that generated experiment
  5. RUN_TYPE: separates testing runs from production runs (all provided data is RUN_TYPE=‘production’)
  6. timestamp: of type - 9/5/2019,9:32:27 AM = month/day/year,hour:minute:second AM/PM
  7. pH: average pH measured over 15 seconds (also provided by Consort)
  8. electrolyte: dictionary specifying composition of electrolyte, with keys: a. volumes : dictionary of volumes (mL) of each feeder solution comprising electrolyte b. ‘source molalities’ : dictionary specifying molality of each feeder solution c. ’source densities’ : dictionary specifying density (g/mL) of each feeder solution
  9. electrochemistry : dictionary giving electrochemical testing data of the electrolyte on Pt electrodes, with keys: a. i: current density array b. V : voltage array (standard hydrogen electrode) c. t: time array (measured from start of test) d. ‘test_name’ : name of electrochemical test method file (PalmSens software) e. ‘derived_quantities’ : dictionary of quantities derived from above array

This dataset was generated at Carnegie Mellon University as part of a project funded by the Toyota Research Institute.

Reference

If using this dataset in a publication, please cite:

Adarsh Dave, Jared Mitchell, Kirthevasan Kandasamy, Han Wang, Sven Burke, Biswajit Paria, Barnabás Póczos, Jay Whitacre, Venkatasubramanian Viswanathan, Autonomous Discovery of Battery Electrolytes with Robotic Experimentation and Machine Learning, Cell Reports Physical Science, Volume 1, Issue 12, 100264 (2020).

Pre-print