西格玛
卷积神经网络
深度学习
特征(语言学)
功能(生物学)
人工智能
计算机科学
航程(航空)
深层神经网络
生物系统
模式识别(心理学)
材料科学
物理
生物
量子力学
语言学
哲学
进化生物学
复合材料
作者
Dinis O. Abranches,Yong Zhang,Edward J. Maginn,Yamil J. Colón
摘要
This work showcases the remarkable ability of sigma profiles to function as molecular descriptors in deep learning. The sigma profiles of 1432 compounds are used to train convolutional neural networks that accurately correlate and predict a wide range of physicochemical properties. The architectures developed are then exploited to include temperature as an additional feature.
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