AI 量子計算
石墨烯准固態電池材料與關鍵技術
SES AI’s approach versus
other approaches
AI 人工智慧輔助
核心技術
The core technology leverages graphene composite materials. The key principle lies in replacing conventional liquid electrolytes with graphene-based solid electrolytes. Apex Graphene plays a pivotal role in this field by developing graphene composites and technologies to identify solid-state electrolytes with high ionic conductivity and chemical stability during the R&D process.
電化學反應機制建模分析
採用多物理場方法分析固態電池的充放電機制：
• 鋰離子傳輸路徑優化：不同於傳統電池中鋰離子通過液態電解質遷移，固態系統中離子必須穿過固態電解質晶格。
• 使用 AI 模擬評估不同固態電解質結構對鋰離子擴散速率的影響，並優化晶體通道設計。
• AI 預測不同溫度下的電化學行為，例如揭示低溫下鋰離子遷移率降低的微觀原因。通過改進石墨烯固態電解質材料，低溫性能得到提升（例如，在-30°C 下容量衰減從傳統電池的 50%降低至 10%）。
界面反應機制分析
固態電池中的電極/電解質界面容易形成高阻抗層，導致性能隨時間衰退。Apex 透過原子級微觀分析揭示失效機制：
利用 AI 量子計算在原子尺度觀察層狀氧化物正極材料中的晶格氧流失、滑移和碎裂現象，揭示界面退化與電池容量衰減之間的關聯。
性能衰退與壽命預測
運用 AI 量子計算與物理建模技術建立電池的數位孿生模型，即時監測微觀材料缺陷（如晶界裂紋或界面副反應），並預測電池循環壽命.
SES AI electrolyte uses a high concentration solvent-in-salt approach. A conventional liquid electrolyte is low concentration, where the salt is coordinated by solvent and there are free solvent molecules, rendering the overall electrolyte stability of Li-Metal poor.
In SES AI's high concentration electrolyte, the solvent is coordinated by the salt, and there are no free solvent molecules. This allows the electrolyte to achieve unprecedented Coulombic efficiency of>99.6% on Li-Metal. Without this high concentration approach, it was thought liquid electrolyte could never achieve such high stability on Li-Metal and high voltage cathode at the same time.
Electrolyte composition
Salt
Maps the vast universe of small molecules and develops an AI Agent to accelerate material discovery for Li-Metal and Li-ion across many applications.
Molecular Universe
Maps the vast universe of small molecules and develops an AI Agent to accelerate material discovery for Li-Metal and Li-ion across many applications.
Molecular Universe
Maps the vast universe of small molecules and develops an AI Agent to accelerate material discovery for Li-Metal and Li-ion across many applications.
Associate Chemist
Associate Chemist
Associate Chemist
Associate Chemist
結論
在固態電池原理研究中，AI 扮演著「智能顯微鏡」與「超級計算引擎」的雙重角色。其不僅加速石墨烯材料發現與界面優化進程，更在原子尺度揭示基礎電化學行為。
透過數據驅動的研發模式，AI 與大數據推動固態電池從實驗室走向量產。藉由高通量篩選、跨領域知識挖掘與自動化實驗驗證，材料發現實現從試錯法到精準設計的轉變。
此方法顯著縮短開發週期（從數年降至數月），解決高能量密度、安全性與成本等關鍵挑戰，並降低產業化風險。
