Diverse polymorphs and phase transitions in van der Waals In$_2$Se$_3$
Mingfeng Liu, Jiantao Wang, Peitao Liu, Qiang Wang, Zhibo Liu, Yan Sun, Xing-Qiu Chen

TL;DR
This study employs machine learning-enhanced molecular dynamics to explore and characterize the complex polymorphic phase space and transitions of van der Waals In$_2$Se$_3$, revealing new polymorphs and phase behavior.
Contribution
We developed a high-accuracy machine-learning potential enabling large-scale MD simulations to uncover new polymorphs and phase transitions in In$_2$Se$_3$ with first-principles accuracy.
Findings
Identified the structure of the $eta''$ polymorph.
Discovered new $eta'$ polymorph variants.
Constructed an ab initio temperature-pressure phase diagram.
Abstract
Van der Waals InSe has garnered significant attention due to its unique properties and wide applications associated with its rich polymorphs and polymorphic phase transitions. Despite extensive studies, the vast complex polymorphic phase space remains largely unexplored, and the underlying microscopic mechanism for their phase transformations remains elusive. Here, we develop a highly accurate, efficient, and reliable machine-learning potential (MLP), which not only facilitates accurate exploration of the intricate potential energy surface (PES), but also enables us to conduct large-scale molecular dynamics (MD) simulations with first-principles accuracy. We identify the accurate structure of the polymorph and uncover several previously unreported polymorph variants exhibiting dynamic stability and competing energies, which are elucidated by characteristic…
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Taxonomy
TopicsSolid-state spectroscopy and crystallography · Chalcogenide Semiconductor Thin Films · Crystal Structures and Properties
