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 recalculating a-C database Deringer Csányi adding van der Waals interactions. Our GAP enables notable speedup improves accuracy energy force predictions. use to thoroughly structure pore-size distribution in computational NP samples. These samples are generated melt-graphitization-quench MD procedure over wide range densities (from 0.5 1.7 g/cm3) with structures containing 131 072 atoms. results good agreement experimental data available observables provide comprehensive account (radial angular functions, motif ring counts, X-ray diffraction patterns, pore characterization) (elastic moduli their evolution density) properties. show relatively narrow distributions, where peak position width distributions dictated mass density materials. allow further work on characterization materials, particular energy-storage applications, as well suggest future carbon-based