Data for Automated Reaction Prediction of Electrolyte Degradation in Highly Concentrated E

Transition state and reaction pathway data for the decomposition of four solvents in high-concentration electrolytes. The data was generated using automated reaction discovery and microkinetic modeling at the ωB97X-D3/def2-TZVPP level of theory. It was authored by Esra Ulgey Akgun and published on figshare in April 2026.

Use Cases

• Training or validating machine learning models for reaction prediction based on transition state data.
• Analyzing decomposition kinetics for specific solvents like ethylene carbonate (EC) and propylene carbonate (PC).
• Studying the role of fluoride (F-) and CF3 fragments in driving solvent degradation pathways.
• Investigating the formation pathways of inorganic LiF and open-ring organic carbonates in battery electrolytes.

Strengths

• Data is openly available under a CC-BY-4.0 license.
• Transition states and IRC pathways are provided at a specific, named level of theory (ωB97X-D3/def2-TZVPP).
• Covers decomposition of four solvents (EC, PC, DMC, AN) with two lithium salts (LiFSI, LiTFSI).

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The 4.9 MB size suggests a relatively small dataset.

Provenance

Source

figshare

Collection Method

Generated via automated reaction discovery and microkinetic modeling (MKM) from computational chemistry simulations.

Time Range

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Freshness

Last updated 2026-04-18 22:41:35; freshness should be verified.

Geography

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