Department of Computer Science, The University of Chicago, Chicago, Illinois 60637-5418, USA.
Toyota Technological Institute at Chicago, Chicago, Illinois 60637-2803, USA.
J Chem Phys. 2018 Jun 28;148(24):241745. doi: 10.1063/1.5024797.
Density functional theory (DFT) is the most successful and widely used approach for computing the electronic structure of matter. However, for tasks involving large sets of candidate molecules, running DFT separately for every possible compound of interest is forbiddingly expensive. In this paper, we propose a neural network based machine learning algorithm which, assuming a sufficiently large training sample of actual DFT results, can instead learn to predict certain properties of molecules purely from their molecular graphs. Our algorithm is based on the recently proposed covariant compositional networks framework and involves tensor reduction operations that are covariant with respect to permutations of the atoms. This new approach avoids some of the representational limitations of other neural networks that are popular in learning from molecular graphs and yields promising results in numerical experiments on the Harvard Clean Energy Project and QM9 molecular datasets.
密度泛函理论（DFT）是计算物质电子结构最成功和广泛使用的方法。然而，对于涉及大量候选分子的任务，对每个感兴趣的可能化合物分别运行 DFT 是非常昂贵的。在本文中，我们提出了一种基于神经网络的机器学习算法，假设具有足够大的实际 DFT 结果训练样本，它可以从分子图中学习来预测分子的某些性质。我们的算法基于最近提出的协变成分网络框架，并涉及张量约简操作，这些操作对于原子的置换是协变的。这种新方法避免了其他在学习分子图时流行的神经网络的一些表示限制，并在哈佛清洁能源项目和 QM9 分子数据集的数值实验中取得了有希望的结果。
