The experimental purpose of X-ray diffraction is to analyze crystalline material structure at the atomic and molecular levels. Such experiments are known as X-ray crystallography. Traditionally, human experts do it with some domain knowledge. X-ray crystallography is useful in numerous domains, e.g., physics, chemistry, and biology. It is tough to own manual physics of diffraction patterns to see a crystal structure with a colossal data set. A convolutional neural network (CNN) is a deep neural network that maps an input image into a high-dimensional space. CNN produces an affordable function for image classification. This paper uses an extension of the convolutional neural network to predict crystal structure from diffraction patterns. We propose a machine-enabled method to predict crystallographic size and space group from a limited number of XRD patterns for small films. We overcome the problem of scarce data within the development of building materials by combining the learning model of moderately monitored equipment, a physics information-enhancing strategy using data generated from the Inorganic Crystal Structure Database, and test data. We compare our approach with a large variety of typical addition as modern machine learning-based classification techniques for crystal structure prediction. Results show that our proposed system outperforms all the baselines by a significant margin for the crystal structure prediction task. Results also show the impact of the number of layers in the all-convolutional neural network architecture for crystal structure prediction.

X 射线衍射的实验目的是在原子和分子水平上分析晶体材料结构。这种实验被称为 X 射线晶体学。传统上，人类专家会利用一些领域知识来完成这项工作。X 射线晶体学可用于许多领域，例如物理学、化学和生物学。很难拥有衍射图案的手动物理学来查看具有庞大数据集的晶体结构。卷积神经网络 (CNN) 是一种将输入图像映射到高维空间的深度神经网络。CNN 为图像分类提供了一个负担得起的功能。本文使用卷积神经网络的扩展来从衍射图案预测晶体结构。我们提出了一种机器支持的方法，可以从有限数量的小薄膜 XRD 图案中预测晶体尺寸和空间群。我们通过结合适度监控设备的学习模型、使用无机晶体结构数据库生成的数据的物理信息增强策略和测试数据，克服了建筑材料开发过程中数据稀缺的问题。我们将我们的方法与各种典型的添加进行比较，作为基于现代机器学习的晶体结构预测分类技术。结果表明，我们提出的系统在晶体结构预测任务上明显优于所有基线。结果还显示了全卷积神经网络架构中层数对晶体结构预测的影响。