LAUSR

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.Organic optoelectronics: Efficient molecules designed by your computerTell your computer the materials properties you need, and it will design the molecule you are looking for. Kyungdoc Kim and colleagues from Samsung and Sungkyunkwan University, Republic of Korea, have developed two computer algorithms that work together for this purpose. The first algorithm looks at a database of known organic molecules and their properties, and finds abstract rules to describe the structure/property relationships; the second one uses these rules to design new molecular structures expected to have the same targeted properties. Using this approach, the researchers have already proposed molecules able to absorb light of a desired color, and materials for the realization of stable and efficient organic displays emitting in the blue. The technique might be applied to discover novel molecules and design rules relevant to a broader range of applications.