Shayeganfar Farzaneh, Shahsavari Rouzbeh
Department of Civil and Environmental Engineering, Rice University, Houston, TX, 77005, USA.
Department of Physics and Energy Engineering, Amirkabir University of Technology, 15916-3967, Tehran, Iran.
Sci Rep. 2021 Jul 23;11(1):15111. doi: 10.1038/s41598-021-94085-9.
Interfacial encoded properties of polymer adlayers adsorbed on the graphene (GE) and silicon dioxide (SiO) have been constituted a scaffold for the creation of new materials. The holistic understanding of nanoscale intermolecular interaction of 1D/2D polymer assemblies on substrate is the key to bottom-up design of molecular devices. We develop an integrated multidisciplinary approach based on electronic structure computation [density functional theory (DFT)] and big data mining [machine learning (ML)] in parallel with neural network (NN) and statistical analysis (SA) to design hybrid polymers from assembly on substrate. Here we demonstrate that interfacial pressure and structural deformation of polymer network adsorbed on GE and SiO offer unique directions for the fabrication of 1D/2D polymers using only a small number of simple molecular building blocks. Our findings serve as the platform for designing a wide range of typical inorganic heterostructures, involving noncovalent intermolecular interaction observed in many nanoscale electronic devices.
吸附在石墨烯（GE）和二氧化硅（SiO）上的聚合物吸附层的界面编码特性已构成了创建新材料的支架。全面理解一维/二维聚合物组装体在基底上的纳米级分子间相互作用是分子器件自下而上设计的关键。我们开发了一种基于电子结构计算[密度泛函理论（DFT）]和大数据挖掘[机器学习（ML）]的综合多学科方法，同时结合神经网络（NN）和统计分析（SA），以从基底上的组装体设计杂化聚合物。在此，我们证明吸附在GE和SiO上的聚合物网络的界面压力和结构变形为仅使用少量简单分子构建单元制备一维/二维聚合物提供了独特的方向。我们的研究结果为设计广泛的典型无机异质结构提供了平台，这些异质结构涉及许多纳米级电子器件中观察到的非共价分子间相互作用。
