卷积神经网络
计算机科学
沸点
人工神经网络
图像(数学)
人工智能
生物系统
熔点
Atom(片上系统)
职位(财务)
算法
分子
模式识别(心理学)
材料科学
化学
物理
热力学
嵌入式系统
复合材料
经济
有机化学
生物
财务
作者
Yawei Xu,Xun Huang,Cunpu Li,Yuan Wei,Meng Wang
摘要
Abstract Machine learning (ML) provides an efficient method to predict the unknown properties during the exploration of new materials, but how to efficiently represent the molecules as input is still not fully solved. Inspired by image processing, one of the classical ML tasks, this work developed a method to predict the structure‐dependent properties by converting the atom position into a three‐dimensional (3D) molecular image and learning the structure features from the image via a classical convolutional neural networks. After trained with datasets larger than 12,000 species, a very high accuracy is obtained in predicting both theoretical molecular energy and experimental properties including melting points, boiling points, and flash points. Since stereoscopic information is explicitly and accurately represented by the molecular images, our model successfully distinguish the melting points and boiling points of molecules with similar structure, including those of trans–cis isomers.
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