Explicit pore pre-conditioning and inverse design of amorphous nanoporous carbon

Abstract

Porous carbons store energy, capture gases and purify water, and their performance depends strongly on the geometry of their pores. Atomistic models mostly simulate their synthesis by molecular dynamics, with results that depend on the interatomic potential, and return the pore network as an output rather than as a specification. Here we show that the pore network can be an input: pores drawn from a target size distribution fill a prescribed volume fraction, carbon is grown around them by stochastic ring placement, and a particle swarm recovers the construction parameters from a structural fingerprint. The search reproduces the density, surface area and pore volume of a carbon produced by quenched molecular dynamics within about 5 %, together with its pore size distribution. As a stress test, it also recovers a bimodal distribution, and the search history reveals which parameters the targets constrain and flags a failed search. A short anneal completes the network, recovering the coordination of the reference while the pore size distribution keeps its shape. Molecular dynamics still supplies the local chemistry, but the pore geometry becomes a design variable for a given application.