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arXiv 2209.01358cond-mat.mtrl-sci
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结合对分布函数与密度泛函理论的基于机器学习的纳米材料原子结构求解方法

Machine learning based approach for solving atomic structures of nanomaterials combining pair distribution functions with density functional theory

Magnus Kløve, Sanna Sommer, Bo B. Iversen, Bjørk Hammer, Wilke Dononelli

AI总结:

提出一种结合密度泛函理论计算与PDF比较的即时训练机器学习算法,用于在人工景观中全局优化并确定纳米晶或无定形化合物(含亚稳态)的未知晶体结构。

摘要:

确定纳米晶或无定形化合物的晶体结构是固体化学和物理学中的一大挑战。X射线或中子全散射数据的对分布函数(PDF)分析已被证明是应对这一挑战的关键要素。然而,在大多数情况下,需要可靠的结构基序作为结构精修的起始构型。在此,我们提出一种算法,能够通过即时训练的机器学习模型确定未知化合物的晶体结构,该模型将密度泛函理论(DFT)计算与计算及测量的PDF比较相结合,用于在人工景观中进行全局优化。由于该景观的特性,甚至可以确定亚稳态构型。

英文摘要:

Determination of crystal structures of nanocrystalline or amorphous compounds is a great challenge in solid states chemistry and physics. Pair distribution function (PDF) analysis of X-Ray or neutron total scattering data has proven to be a key element in tackling this challenge. However, in most cases a reliable structural motif is needed as starting configuration for structure refinements. Here, we present an algorithm that is able to determine the crystal structure of an unknown compound by means of an on-the-fly trained machine learning model that combines density functional theory (DFT) calculations with comparison of calculated and measured PDFs for global optimization in an artificial landscape. Due to the nature of this landscape, even metastable configurations can be determined.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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