自律的探索型研究が電池開発にパラダイム転換を促す(Autonomous discovery-driven Argonne study inspires paradigm shift in battery research)

2026-01-13 アルゴンヌ国立研究所(ANL)

米国のArgonne National Laboratory(アルゴンヌ国立研究所)の研究チームは、人工知能(AI)とロボット技術を活用した自律実験(autonomous discovery)により、従来の手法では数年かかるバッテリー化学実験をわずか数か月で実施することに成功し、バッテリー材料研究におけるパラダイムシフトを示した。研究ではAIと自動化された高スループット実験系を用いて、6,000以上の電池化学実験を5か月で実行したと報告されている。この自律実験アプローチは、材料探索の高速化とリソース削減を可能にし、蓄電池設計の最適化を劇的に進める潜在力を持つ。こうした技術は、次世代バッテリー技術の開発と実用化を加速し、エネルギー貯蔵システムの性能向上に貢献すると期待される。

<関連情報>

ハイスループット発見により、有機電解質中の長寿命荷電種の設計原理と限界が明らかに High-Throughput Discovery Illuminates Design Principles and Limits for Long-Lived Charged Species in Organic Electrolytes

Lily A. Robertson,Ilya A. Shkrob,Ryan Lewis,Logan Ward,Rafael Vescovi,and Benjamin T. Diroll
Journal of American Chemical Society  Published September 18, 2025
DOI:https://doi.org/10.1021/jacs.5c10140

Abstract

 

自律的探索型研究が電池開発にパラダイム転換を促す(Autonomous discovery-driven Argonne study inspires paradigm shift in battery research)

The chemical stability of charged molecules in all-organic redox flow batteries (RFBs) is required for the prolonged operation of these devices. Molecular engineering and electrolyte optimization are used to mitigate parasitic reactions and extend the lifetimes of the charge carriers. However, how much can structural variation extend the lifetime? To probe this query, we designed a high-throughput kinetic study of the radical cation of N-methylphenothiazinium, guided by statistical sampling and learning algorithms. Using Argonne’s autonomous discovery facility, we conducted over 6,000 kinetic experiments with robotic sample preparation, parallel kinetic measurements, and machine learning inputs, testing 188 solvent molecules selected from a space of over 540 candidates from 11 chemical classes. Algorithmic selections guided us to stable solvent candidates, which were further tested in high concentration with and without supporting electrolyte. Our findings reveal the inherent difficulty of exceeding the current state of the art through solvent variation. The desired stability is statistically rare and poorly predictable. Among the many tested, only three solvents significantly outperformed our baseline, acetonitrile─and none by more than a factor of 3─suggesting a general challenge in achieving the necessary techno-economic targets. We suggest that self-discharge through solvent homolysis is the cause of the observed limitations. Several structural motifs contribute to >1,000 h half-life stability including molecular simplicity, symmetry, oxidation complement, and strategic fluorination. Importantly, this workflow establishes effective assays for diagnosing and predicting oxidative stress for highly stable liquid electrolytes in all batteries.

0402電気応用
ad
ad
Follow
ad
タイトルとURLをコピーしました