# Machine learning model improves battery electrolyte simulations against experimental tests

_Published Wednesday, October 14, 2026 at 12:20 PM EDT · Science, AI · Latest · Tier 2 — Notable_

Researchers reporting in Nature demonstrated a machine learning model of lithium-ion battery electrolytes that reproduced experimental properties and revealed lithium-ion clusters missed by a conventional simulation model. Nuclear magnetic resonance measurements and quantum-chemical calculations supported the predicted structures.

The model learns atomic energies from quantum-chemical calculations of gas-phase clusters, then simulates liquid electrolytes. Its calculated association energies agreed with the training-level quantum calculations within chemical accuracy, while the conventional model's errors were 10-100-times larger. The approach costs two to three orders of magnitude more per energy evaluation than the conventional model, but remains many orders of magnitude less expensive than a purely quantum-chemical approach.

## Sources

- [Nature](https://www.nature.com/articles/s41586-026-11138-7)

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Canonical: https://techandbusiness.org/newswire/1e6CXSEN_wlrAuJb3pUyZK
Published: 2026-10-14T16:20:36.444Z
Story chronology: 2026-10-14T00:00:00.000Z
Retrieved: 2026-10-14T19:08:09.481Z
Publisher: Tech & Business (techandbusiness.org)