石墨烯
氧化物
X射线光电子能谱
计算机科学
分子动力学
在飞行中
反应性(心理学)
纳米技术
加速度
材料科学
纳米
物理
化学
计算化学
医学
替代医学
核磁共振
病理
经典力学
冶金
操作系统
复合材料
作者
Zakariya El-Machachi,Damyan Frantzov,A. Nijamudheen,Tigany Zarrouk,A. Miguel,Volker L. Deringer
标识
DOI:10.1002/anie.202410088
摘要
Graphene oxide (GO) materials are widely studied, and yet their atomic-scale structures remain to be fully understood. Here we show that the chemical and configurational space of GO can be rapidly explored by advanced machine-learning methods, combining on-the-fly acceleration for first-principles molecular dynamics with message-passing neural-network potentials. The first step allows for the rapid sampling of chemical structures with very little prior knowledge required; the second step affords state-of-the-art accuracy and predictive power. We apply the method to the thermal reduction of GO, which we describe in a realistic (ten-nanometre scale) structural model. Our simulations are consistent with recent experimental findings, including X-ray photoelectron spectroscopy (XPS), and help to rationalise them in atomistic and mechanistic detail. More generally, our work provides a platform for routine, accurate, and predictive simulations of diverse carbonaceous materials.
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