Lindsey Rebecca K, Goldman Nir, Fried Laurence E, Bastea Sorin
Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA, 94550, USA.
Department of Chemical Engineering, University of California, Davis, CA, 95616, USA.
Nat Commun. 2022 Mar 17;13(1):1424. doi: 10.1038/s41467-022-29024-x.
There is significant interest in establishing a capability for tailored synthesis of next-generation carbon-based nanomaterials due to their broad range of applications and high degree of tunability. High pressure (e.g., shockwave-driven) synthesis holds promise as an effective discovery method, but experimental challenges preclude elucidating the processes governing nanocarbon production from carbon-rich precursors that could otherwise guide efforts through the prohibitively expansive design space. Here we report findings from large scale atomistically-resolved simulations of carbon condensation from C/O mixtures subjected to extreme pressures and temperatures, made possible by machine-learned reactive interatomic potentials. We find that liquid nanocarbon formation follows classical growth kinetics driven by Ostwald ripening (i.e., growth of large clusters at the expense of shrinking small ones) and obeys dynamical scaling in a process mediated by carbon chemistry in the surrounding reactive fluid. The results provide direct insight into carbon condensation in a representative system and pave the way for its exploration in higher complexity organic materials. They also suggest that simulations using machine-learned interatomic potentials could eventually be employed as in-silico design tools for new nanomaterials.
由于下一代碳基纳米材料具有广泛的应用范围和高度的可调节性，人们对建立其定制合成能力有着浓厚的兴趣。高压（例如，冲击波驱动）合成有望成为一种有效的发现方法，但实验挑战使得难以阐明从富含碳的前驱体中产生纳米碳的过程，而这些过程原本可以指导人们在极其广阔的设计空间中开展工作。在此，我们报告了通过机器学习反应性原子间势实现的对C/O混合物在极端压力和温度下碳凝聚的大规模原子分辨率模拟结果。我们发现，液态纳米碳的形成遵循由奥斯特瓦尔德熟化驱动的经典生长动力学（即大团簇的生长以小团簇的收缩为代价），并且在周围反应性流体中碳化学介导的过程中遵循动态标度。这些结果为具有代表性的系统中的碳凝聚提供了直接的见解，并为在更高复杂性的有机材料中进行探索铺平了道路。它们还表明，使用机器学习原子间势的模拟最终可作为新型纳米材料的计算机辅助设计工具。
