Data science shows that entropy correlates with accelerated zeolite crystallization in Monte Carlo simulations DOI

Seungbo Hong,

Giovanni Pireddu, Wei Fan

et al.

The Journal of Chemical Physics, Journal Year: 2024, Volume and Issue: 161(23)

Published: Dec. 18, 2024

We have performed a data science study of Monte Carlo (MC) simulation trajectories to understand factors that can accelerate the formation zeolite nanoporous crystals, process take days or even weeks. In previous work, MC simulations predicted and experiments confirmed using secondary organic structure-directing agent (OSDA) accelerates crystallization all-silica LTA zeolite, with finding three-fold speedup [Bores et al., Phys. Chem. 24, 142–148 (2022)]. However, it remains unclear what physical cause speed-up. Here, we apply analyze discover drives accelerated in going from one-OSDA synthesis (1OSDA) two-OSDA version (2OSDA). encoded snapshots smooth overlap atomic positions approach, which represents all two- three-body correlations within given cutoff distance. Principal component analyses failed discriminate datasets structures 1OSDA 2OSDA simulations, while Support Vector Machine (SVM) approach succeeded at classifying such an area-under-curve (AUC) score 0.99 (where AUC = 1 is perfect classification) as high 0.94 only two-body correlations. SVM decision functions reveal relatively broad/narrow histograms for 1OSDA/2OSDA datasets, suggesting two differ strongly information heterogeneity. Informed by these results, pair (2-body) entropy calculations during crystallization, resulting differences semi-quantitatively account observed simulations. conclude altering conditions ways substantially change labile silica networks may discuss possible approaches achieving acceleration.

Language: Английский

Data science shows that entropy correlates with accelerated zeolite crystallization in Monte Carlo simulations DOI

Seungbo Hong,

Giovanni Pireddu, Wei Fan

et al.

The Journal of Chemical Physics, Journal Year: 2024, Volume and Issue: 161(23)

Published: Dec. 18, 2024

We have performed a data science study of Monte Carlo (MC) simulation trajectories to understand factors that can accelerate the formation zeolite nanoporous crystals, process take days or even weeks. In previous work, MC simulations predicted and experiments confirmed using secondary organic structure-directing agent (OSDA) accelerates crystallization all-silica LTA zeolite, with finding three-fold speedup [Bores et al., Phys. Chem. 24, 142–148 (2022)]. However, it remains unclear what physical cause speed-up. Here, we apply analyze discover drives accelerated in going from one-OSDA synthesis (1OSDA) two-OSDA version (2OSDA). encoded snapshots smooth overlap atomic positions approach, which represents all two- three-body correlations within given cutoff distance. Principal component analyses failed discriminate datasets structures 1OSDA 2OSDA simulations, while Support Vector Machine (SVM) approach succeeded at classifying such an area-under-curve (AUC) score 0.99 (where AUC = 1 is perfect classification) as high 0.94 only two-body correlations. SVM decision functions reveal relatively broad/narrow histograms for 1OSDA/2OSDA datasets, suggesting two differ strongly information heterogeneity. Informed by these results, pair (2-body) entropy calculations during crystallization, resulting differences semi-quantitatively account observed simulations. conclude altering conditions ways substantially change labile silica networks may discuss possible approaches achieving acceleration.

Language: Английский

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