Optimization of Molecular Characteristics via Machine Learning Based on Continuous Representation of Molecules

Abstract

We demonstrate an automatic materials design method using continuous representation of molecule and its atomic arrangement via a neural network algorithm. This method is applied to optimizing and predicting the HOMO-LUMO gap within the molecules composed of carbon, oxygen, nitrogen, fluorine, and hydrogen. Adopting the Quantum Machine 9 (QM9) dataset as a training dataset for the molecules, we first established a continuous representation of molecules in a latent space, then predicted molecules that have target values of the HOMO-LUMO gap. In the gap maximization calculation, the CF4 with the largest gap value in the QM9 dataset was automatically found despite there is no a priori data for the gap. In the case of a target gap value of 0.10 hartree, we found a new molecule whose gap value is closer to 0.10 hartree than any other molecules in the QM9 dataset.