Structure and pore size distribution in nanoporous carbon
We study the structural and mechanical properties of nanoporous (NP) carbon materials by extensive atomistic machine-learning (ML) driven molecular dynamics (MD) simulations. To this end, we retrain a ML Gaussian approximation potential (GAP) for carbon by recalculating the a-C structural database of Deringer and Csányi adding van der Waals interactions. Our GAP enables a notable speedup and improves the accuracy of energy and force predictions. We use the GAP to thoroughly study the atomistic structure and pore-size distribution in computational NP carbon samples. These samples are generated by a melt-graphitization-quench MD procedure over a wide range of densities (from 0.5 to 1.7 g/cm3) with structures containing 131072 atoms. Our results are in good agreement with experimental data for the available observables and provide a comprehensive account of structural (radial and angular distribution functions, motif and ring counts, X-ray diffraction patterns, pore characterization) and mechanical (elastic moduli and their evolution with density) properties. Our results show relatively narrow pore-size distributions, where the peak position and width of the distributions are dictated by the mass density of the materials. Our data allow further work on computational characterization of NP carbon materials, in particular for energy-storage applications, as well as suggest future experimental characterization of NP carbon-based materials.
Funding
Academy of Finland, under projects 321713, 330488, 312298/QTF Center of Excellence program
China Scholarship Council under Grant No. CSC202006460064
History
School
- Science
Department
- Mathematical Sciences
Published in
Chemistry of MaterialsVolume
34Issue
2Pages
617 - 628Publisher
American Chemical Society (ACS)Version
- VoR (Version of Record)
Rights holder
© The AuthorsPublisher statement
This is an Open Access Article. It is published by American Chemical Society under the Creative Commons Attribution 4.0 International Licence (CC BY). Full details of this licence are available at: https://creativecommons.org/licenses/by/4.0/Publication date
2022-01-04Copyright date
2022ISSN
0897-4756eISSN
1520-5002Publisher version
Language
- en