By focusing on electrolytes, MIT scientists are making sodium-metal batteries a more practical energy storage option.
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The paper introduces EMolStudio, a density‑matrix‑centered AI platform that predicts and analyzes electronic structure for electrolyte molecules in lithium batteries. It integrates molecular functionalization, explicit Li⁺ first‑shell assembly, density‑matrix prediction, and electronic‑structure parsing, enabling systematic study of 163,655 functionalized molecules and 22,500 first‑shell clusters across four lithium salts. The analysis reveals that functional groups and anion identity distinctly influence frontier orbital energies, electrostatic potential, and Li⁺‑donor contacts, with LiTDI anchoring the highest occupied orbital on the anion.
The paper presents a methodological framework that links the FINALES experiment orchestration system with the Kadi4Mat research data management ecosystem to create interoperable, automated workflows for battery research. By coordinating experiment planning, execution, data handling, and analysis across distributed sites, the framework enables reproducible, end‑to‑end experimental pipelines. The authors demonstrate its utility by studying sodium‑ion coin cell formation, using a Gaussian process model to map formation parameters to electrochemical performance and identify promising experimental regions.
The increasing share of renewable energy in power systems creates a need for fast-response and flexible resources to maintain system stability. With the expansion of electricity markets and ancillary...
arXiv:2608. 16238v1 Announce Type: new Abstract: The increasing share of renewable energy in power systems creates a need for fast-response and flexible resources to maintain system stability.
ProtoMI is a literature‑driven framework that learns structural priors from 126 reported boron‑containing electrolyte additives and applies them to screen 179,977 unlabeled candidates. Using graph contrastive learning, it identifies seven interpretable prototypes and adapts them through semi‑supervised contrastive learning, achieving enrichment factors of 9.2–45.6 while evaluating less than 2% of the candidate space. The method led to the discovery of four commercially accessible additives, including TNDB, which improves high‑temperature LiFePO4||graphite cycling by 34.93% and forms protective interphases that suppress solvent decomposition and Fe deposition.
arXiv:2607. 25597v1 Announce Type: new Abstract: The reactivity of lithium-metal electrolytes arises from the interplay of molecular functional groups, Li$^+$ solvation, and salt-anion participation.