Revisiting Percolation and Molecular Diffusion in Disordered Pore Networks
- 1Chemical and Biological Engineering
- 2State University of New York at Buffalo
Abstract
The relationship between pore network structure and molecular diffusion in disordered nanoporous materials remains unclear. Here, we revisit two methodological choices that may contribute to inconsistent structure–diffusion relationships in the literature: percolation characterization and inclusion of non-percolating pores in diffusivity calculations. Our rigorous benchmarks show consistent results from Zeo++ and Mofography but reveal biased characterization from PoreBlazer due to limitations in its algorithm. Molecular dynamics simulations further consolidate that initializing adsorbates in percolating pore networks is essential for reliable and robust calculations of molecular self-diffusivity. With these issues addressed, we re-examine structure–diffusion relationships for methane across four amorphous material classes. Symbolic regression identifies the percolating backbone fraction as a key, yet largely overlooked, topology-related descriptor that captures a common diffusion trend across classes. Further analysis supports the added predictive value of this new descriptor beyond conventional geometric descriptors in machine learning tasks. Our work provides a consistent methodological foundation and a complementary descriptor for diffusion prediction in disordered nanoporous materials.
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BibTeX
@misc{zheng2026revisiting,
title = {{Revisiting Percolation and Molecular Diffusion in Disordered Pore Networks}},
author = {Chao Zheng and Arun Gopalan and Kaihang Shi},
howpublished = {ChemRxiv},
year = {2026},
month = oct,
doi = {10.26434/chemrxiv.15011012/v1},
url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15011012/v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Revisiting Percolation and Molecular Diffusion in Disordered Pore Networks
AU - Chao Zheng
AU - Arun Gopalan
AU - Kaihang Shi
PY - 2026
DA - 2026/10/25
PB - ChemRxiv
DO - 10.26434/chemrxiv.15011012/v1
UR - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15011012/v1
AB - The relationship between pore network structure and molecular diffusion in disordered nanoporous materials remains unclear. Here, we revisit two methodological choices that may contribute to inconsistent structure–diffusion relationships in the literature: percolation characterization and inclusion of non-percolating pores in diffusivity calculations. Our rigorous benchmarks show consistent results from Zeo++ and Mofography but reveal biased characterization from PoreBlazer due to limitations in its algorithm. Molecular dynamics simulations further consolidate that initializing adsorbates in percolating pore networks is essential for reliable and robust calculations of molecular self-diffusivity. With these issues addressed, we re-examine structure–diffusion relationships for methane across four amorphous material classes. Symbolic regression identifies the percolating backbone fraction as a key, yet largely overlooked, topology-related descriptor that captures a common diffusion trend across classes. Further analysis supports the added predictive value of this new descriptor beyond conventional geometric descriptors in machine learning tasks. Our work provides a consistent methodological foundation and a complementary descriptor for diffusion prediction in disordered nanoporous materials.
ER -