Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, Indiana 46556, USA.
Chem Commun (Camb). 2022 May 5;58(37):5630-5633. doi: 10.1039/d2cc01549h.
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.
这项工作展示了 sigma 轮廓在深度学习中作为分子描述符的出色能力。使用 1432 种化合物的 sigma 轮廓来训练卷积神经网络，这些网络可以准确地关联和预测广泛的物理化学性质。然后，利用所开发的架构将温度作为附加特征纳入其中。
