Polymorphic crystallites model for monolayer amorphous materials
Abstract
Establishing atomistic models of amorphous materials remains a central challenge in materials science. Here, we examine three representative multicomponent monolayer systems using first-principles-trained machine-learning potentials (MLPs) and an energy-guided Monte Carlo structural-search workflow. For monolayer amorphous boron nitride (maBN), independent density-functional-theory (DFT) single-point calculations confirm that the ensemble generated by the MLP-driven search is lower in energy on average than an extended-Tersoff-generated ensemble. Across the sampled maBN structures, local-ring classification combined with strict connected-domain analysis identifies h-BN-like, o-B2N2-like, and o-B4N4-like crystallite domains together with mixed medium-range order. Only individual samples containing at least two distinct phase-pure crystallite-domain types are classified as polymorphic crystallites. The same analysis finds coexisting hexagonal and tetragonal domains in monolayer amorphous LiCl and graphene-like, h-BN-like, borophene-like, and mixed-order regions in monolayer amorphous BCN. Five-nanosecond MLP molecular-dynamics trajectories at 300 K show that the candidate structures are dynamically persistent on the simulated timescale. These results support a polymorphic-crystallite description for the selected model systems, while experimental discrimination from alternative continuous-random-network descriptions will require medium-range-order-sensitive measurements.
Versions
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BibTeX
@misc{zhu2026polymorphic,
title = {{Polymorphic crystallites model for monolayer amorphous materials}},
author = {Le-Ye Zhu and Xi Zhang and Yun-Peng Wang and Jieheng Shi and Junwei Zhang and Shixuan Du and Yu-Yang Zhang},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2605.01881},
eprint = {2605.01881},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2605.01881},
}
RIS
TY - UNPB
TI - Polymorphic crystallites model for monolayer amorphous materials
AU - Le-Ye Zhu
AU - Xi Zhang
AU - Yun-Peng Wang
AU - Jieheng Shi
AU - Junwei Zhang
AU - Shixuan Du
AU - Yu-Yang Zhang
PY - 2026
DA - 2026/09/17
PB - arXiv
DO - 10.48550/arxiv.2605.01881
UR - https://arxiv.org/abs/2605.01881
AB - Establishing atomistic models of amorphous materials remains a central challenge in materials science. Here, we examine three representative multicomponent monolayer systems using first-principles-trained machine-learning potentials (MLPs) and an energy-guided Monte Carlo structural-search workflow. For monolayer amorphous boron nitride (maBN), independent density-functional-theory (DFT) single-point calculations confirm that the ensemble generated by the MLP-driven search is lower in energy on average than an extended-Tersoff-generated ensemble. Across the sampled maBN structures, local-ring classification combined with strict connected-domain analysis identifies h-BN-like, o-B_2N_2-like, and o-B_4N_4-like crystallite domains together with mixed medium-range order. Only individual samples containing at least two distinct phase-pure crystallite-domain types are classified as polymorphic crystallites. The same analysis finds coexisting hexagonal and tetragonal domains in monolayer amorphous LiCl and graphene-like, h-BN-like, borophene-like, and mixed-order regions in monolayer amorphous BCN. Five-nanosecond MLP molecular-dynamics trajectories at 300 K show that the candidate structures are dynamically persistent on the simulated timescale. These results support a polymorphic-crystallite description for the selected model systems, while experimental discrimination from alternative continuous-random-network descriptions will require medium-range-order-sensitive measurements.
ER -