Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, UK.
Chem Commun (Camb). 2018 Jun 8;54(47):5988-5991. doi: 10.1039/c8cc01388h.
Disordered nanoporous and "hard" carbons are widely used in batteries and supercapacitors, but their atomic structures are poorly determined. Here, we combine machine learning and DFT to obtain new atomistic insight into carbonaceous energy materials. We study structural models of porous and graphitic carbons, and Na intercalation as relevant for sodium-ion batteries.
无序纳米多孔和“硬”碳广泛应用于电池和超级电容器，但它们的原子结构仍不清楚。在这里，我们结合机器学习和 DFT 为碳质能源材料提供新的原子水平的见解。我们研究了多孔和石墨碳的结构模型，以及钠离子电池中相关的钠离子嵌入。
