论文标题
基于机器学习的方法，用于解决纳米材料的原子结构，将配对分布函数与密度功能理论结合起来
Machine learning based approach for solving atomic structures of nanomaterials combining pair distribution functions with density functional theory
论文作者
论文摘要
纳米晶或无定形化合物的晶体结构的测定是固态化学和物理学的巨大挑战。事实证明，X射线或中子总散射数据的配对分布函数（PDF）分析是应对这一挑战的关键要素。但是，在大多数情况下，需要一个可靠的结构基序作为结构改进的启动配置。在这里，我们提出了一种算法，该算法能够通过训练有素的机器学习模型来确定未知化合物的晶体结构，该模型将密度功能理论（DFT）计算与计算和测量的PDF进行比较，以在人工景观中进行全局优化。由于这种景观的性质，甚至可以确定亚稳态的配置。
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.
