新計算方法加速再生能源材料的發現
New Computing Method Speeds Up Discovery of Renewable Energy Materials
向再生能源的過渡取決於為電池與太陽能電池尋找先進材料。
The transition to renewable energy hinges on finding advanced materials for batteries and solar cells.
透過利用人工智慧（AI）和機器學習，研究人員現在可以在計算上篩選數千種化學候選物。
By leveraging Artificial Intelligence (AI) and machine learning, researchers can now screen thousands of chemical candidates computationally.
這種方法使用高通量虛擬篩選與密度泛函理論（DFT）來高精度地建模原子結構。
This approach uses high-throughput virtual screening and Density Functional Theory (DFT) to model atomic structures with high precision.
此外，將AI與機器人技術結合的「自主駕駛」實驗室，能夠不間斷地在二十四小時內測試這些材料，且無需人工干預。
Furthermore, 'self-driving' labs, which combine AI with robotics, can test these materials 24/7 without human intervention.
儘管像數據質量和複雜的現實合成等挑戰依然存在，但這些強大工具的整合正在為綠色能源創新打造一個具備擴展性的引擎。
While challenges like data quality and complex real-world synthesis remain, the integration of these powerful tools is creating a scalable engine for green energy innovation.
