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

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.

Selective Growth of ZnO Nanorods on SiO2/Si Substrates Using a Graphene Buffer Layer

为用在一个低温度答案过程的 graphene 缓冲区层的 SiO2/Si 底层上的 ZnO nanorods 的选择生长的有希望的策略被描述。ZnO nanorods 的高密度在一个大区域上被种，大多数 ZnO nanorods 在 graphene 上是垂直地排列得好的。而且， graphene 上的 ZnO nanorods 的选择生长被使用一个简单机械处理认识到，自从在 graphene 上形成的 ZnO nanorods 在原子水平上是机械地稳定的。这些结果被证明 ZnO-graphene 绑定有一个低 destabilization 精力的第一原则计算证实。另外，与 graphene 缓冲区层在 SiO2/Si 上种的 ZnO nanorods 比在赤裸的 SiO2/Si 上种的 ZnO nanorods 有更好光的性质，这被发现。nanostructured ZnO-graphene 材料在未来有有希望的应用灵活电子、光的设备。