Predicting molecular properties with covariant compositional networks
Abstract
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jun 27, 2018
- Source ID
- 10.1063/1.5024797
Entities
People
- Brandon M. Anderson
- Horace Pan
- Risi Kondor
- Shubhendu Trivedi
- Truong Son Hy
Organizations
- Defense Advanced Research Projects Agency
- Toyota Technological Institute
- University of Chicago