Vitriflow: calibrated amorphous structure ensembles from melt-quench simulation
Abstract
Many structure-property relations in amorphous materials are encoded not only in the mean structure, but in the prevalence, correlations and spatial organisation of minority environments across length scales. Yet atomistic models are commonly validated against bulk averages and a nominal number of structures, without establishing whether the finite population resolves the motifs invoked to explain material behaviour. We present an auditable framework, implemented in the Vitriflow software package, that connects calibrated melt-quench generation to explicit populations and quantity-specific uncertainty. This approach is demonstrated on three distinct benchmark systems. In a-SiO2, strain is followed from intra-tetrahedral deformation through compressed Si--O--Si bridges to primitive 3-ring topology, showing that the lower angular tail is an organised medium-range population rather than undifferentiated variation about the network mean. In matched a-Si3N4 refinements, DFT redistributes density and first-neighbour length scale, sharpens the force-field first shell and repairs marginal contacts, while the two DFT descendants retain essentially the same first-neighbour topology. In a-Sm2O3, the amount and connectivity of partially ordered domains are quantified as they emerge from the disordered parent population, while the mixed coordination characteristic of the amorphous oxide remains robust. Mean structure, distribution tails, inherited topology and connected order are therefore distinct materials variables with distinct uncertainties. There is no universal ensemble size: the Vitriflow package records what was generated, which population was analysed and what that finite population establishes, providing a reproducible route from amorphous structure datasets to quantitatively supported structure-property hypotheses.
Versions
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BibTeX
@misc{cottom2026vitriflow,
title = {{Vitriflow: calibrated amorphous structure ensembles from melt-quench simulation}},
author = {Jonathon Cottom and Robin Delhomme and Emilia Olsson},
howpublished = {arXiv},
year = {2026},
month = aug,
doi = {10.48550/arxiv.2607.01407},
eprint = {2607.01407},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2607.01407},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Vitriflow: calibrated amorphous structure ensembles from melt-quench simulation
AU - Jonathon Cottom
AU - Robin Delhomme
AU - Emilia Olsson
PY - 2026
DA - 2026/08/31
PB - arXiv
DO - 10.48550/arxiv.2607.01407
UR - https://arxiv.org/abs/2607.01407
AB - Many structure-property relations in amorphous materials are encoded not only in the mean structure, but in the prevalence, correlations and spatial organisation of minority environments across length scales. Yet atomistic models are commonly validated against bulk averages and a nominal number of structures, without establishing whether the finite population resolves the motifs invoked to explain material behaviour. We present an auditable framework, implemented in the Vitriflow software package, that connects calibrated melt-quench generation to explicit populations and quantity-specific uncertainty. This approach is demonstrated on three distinct benchmark systems. In a-SiO_2, strain is followed from intra-tetrahedral deformation through compressed Si--O--Si bridges to primitive 3-ring topology, showing that the lower angular tail is an organised medium-range population rather than undifferentiated variation about the network mean. In matched a-Si_3N_4 refinements, DFT redistributes density and first-neighbour length scale, sharpens the force-field first shell and repairs marginal contacts, while the two DFT descendants retain essentially the same first-neighbour topology. In a-Sm_2O_3, the amount and connectivity of partially ordered domains are quantified as they emerge from the disordered parent population, while the mixed coordination characteristic of the amorphous oxide remains robust. Mean structure, distribution tails, inherited topology and connected order are therefore distinct materials variables with distinct uncertainties. There is no universal ensemble size: the Vitriflow package records what was generated, which population was analysed and what that finite population establishes, providing a reproducible route from amorphous structure datasets to quantitatively supported structure-property hypotheses.
ER -