Deciphering borophene growth pathways with data-driven simulations
Abstract
Deterministic synthesis of borophene remains challenging because many polymorphs compete during nucleation and growth. Here we combine a reactive machine-learned interatomic potential with grand-canonical Monte Carlo simulations and data-driven structural classification to track borophene formation from early nuclei to extended layers on Ag(111) and Ag(100). We build temperature-pressure substrate growth maps and resolve how vacancy motifs, phase intermixing and seed structure govern polymorph selection. The simulations reproduce key experimental trends, including the prevalence of β12/χ3 phases and their temperature-dependent competition, while revealing kinetic pathways that connect metastable nuclei to long-range order. We identify conditions that suppress competing motifs and promote targeted phases, providing actionable synthesis windows. These results establish a predictive framework for directing borophene growth and, more broadly, for controlling polymorphism in low-dimensional materials by coupling atomistic simulation with machine-learning-enabled phase recognition.
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BibTeX
@misc{bousige2026deciphering,
title = {{Deciphering borophene growth pathways with data-driven simulations}},
author = {Colin Bousige and Jean Furstoss and Julien Lam and Pierre Mignon},
howpublished = {HAL},
year = {2026},
month = jul,
doi = {10.48550/arxiv.2606.03886},
eprint = {2606.03886},
archivePrefix = {arXiv},
url = {https://hal.science/hal-05842241v1},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - Deciphering borophene growth pathways with data-driven simulations
AU - Colin Bousige
AU - Jean Furstoss
AU - Julien Lam
AU - Pierre Mignon
PY - 2026
DA - 2026/07/01
PB - HAL
DO - 10.48550/arxiv.2606.03886
UR - https://hal.science/hal-05842241v1
AB - Deterministic synthesis of borophene remains challenging because many polymorphs compete during nucleation and growth. Here we combine a reactive machine-learned interatomic potential with grand-canonical Monte Carlo simulations and data-driven structural classification to track borophene formation from early nuclei to extended layers on Ag(111) and Ag(100). We build temperature-pressure substrate growth maps and resolve how vacancy motifs, phase intermixing and seed structure govern polymorph selection. The simulations reproduce key experimental trends, including the prevalence of β_12/χ_3 phases and their temperature-dependent competition, while revealing kinetic pathways that connect metastable nuclei to long-range order. We identify conditions that suppress competing motifs and promote targeted phases, providing actionable synthesis windows. These results establish a predictive framework for directing borophene growth and, more broadly, for controlling polymorphism in low-dimensional materials by coupling atomistic simulation with machine-learning-enabled phase recognition.
ER -