Deep-learning-based inverse design model for intelligent discovery of organic molecules

TLDR

The discovery of high‑performance functional materials is crucial for overcoming technical issues in modern industries, and machine learning offers efficient exploration without time‑consuming iterations. We develop an inverse design model based on a deep encoder‑decoder architecture for targeted molecular design. Inspired by neural machine translation, the encoder extracts hidden features linking molecular structures to properties, and the decoder reconstructs new molecules with target properties. The fully data‑driven method learns design rules from databases and generates promising light‑absorbing molecules and host materials for phosphorescent OLEDs by creating new ligands and combinatorial rules.

Abstract

Abstract The discovery of high-performance functional materials is crucial for overcoming technical issues in modern industries. Extensive efforts have been devoted toward accelerating and facilitating this process, not only experimentally but also from the viewpoint of materials design. Recently, machine learning has attracted considerable attention, as it can provide rational guidelines for efficient material exploration without time-consuming iterations or prior human knowledge. In this regard, here we develop an inverse design model based on a deep encoder-decoder architecture for targeted molecular design. Inspired by neural machine language translation, the deep neural network encoder extracts hidden features between molecular structures and their material properties, while the recurrent neural network decoder reconstructs the extracted features into new molecular structures having the target properties. In material design tasks, the proposed fully data-driven methodology successfully learned design rules from the given databases and generated promising light-absorbing molecules and host materials for a phosphorescent organic light-emitting diode by creating new ligands and combinatorial rules.