ConfPred: A layered intergrowth structure prediction model based on confinement self-assembly in two-dimensional interlayer space
Hao Jiang, HuiXiang Chen, GuangHan Cao

TL;DR
ConfPred is a novel layered structure prediction model that uses confinement self-assembly in two-dimensional space, enabling efficient discovery of intergrowth structures, including complex multi-element compounds, with improved accuracy and speed.
Contribution
This paper introduces a new structure prediction model based on confinement self-assembly, enhancing the search for layered intergrowth materials within known layered frameworks.
Findings
Successfully predicts existing iron-based superconductor structures.
Identifies several new stable and metastable layered structures.
Demonstrates efficiency in multi-element structure prediction.
Abstract
We constructed a simple but effective model to predict the layered intergrowth structures by combining the self-assembly phenomenon in confined space and the sandwich configuration of layered materials. In this model, a two-dimensional confined space is constructed by two known block layers, such as the FeAs block of iron-based superconductors. Then, the crystal structure prediction is carried out only inside the confined space to search for brand-new block layers. We realized this model on the basis of the USPEX9.4 code. In the test, the already existing iron-based superconductors can be always successfully found, such as BaTiFeAsO, SrScFeAsO, SrVFeAsO, and so on. The comparison test suggests that our model has remarkable advantages in searching for intergrowth structures. With this space confinement prediction model, a…
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Taxonomy
TopicsIron-based superconductors research · Intellectual Capital and Performance Analysis
