The phase behaviours of a lamellar diblock copolymer/nanorod composite under steady shear are investigated using dissipative particle dynamics. We consider a wide range of nanorod concentrations, where the nanorods ea...The phase behaviours of a lamellar diblock copolymer/nanorod composite under steady shear are investigated using dissipative particle dynamics. We consider a wide range of nanorod concentrations, where the nanorods each have a preferential affinity to one of the blocks. Our results suggest that shear not only aligns the orientations of the diblock eopolymer templates and nanorods towards flow direction, but also regulates the distribution of the nanorods within the polymer matrix. Meanwhile, the shear-induced reorientation and morphology transitions of the systems also significantly depend on the nanorod concentration. At certain nanorod concentrations, the competitions between shearinduced polymer thinning and nanorods dispersion behaviours determine the phase behaviours of the composites. For high nanorod concentrations, no morphology transition is observed, but reorientation is present, in which the sheared nanorods are arranged into hexagonal packing arrays. Additionally, the orientation behaviour of nanorods is determined directly by the applied shear, also interfered with by the shear-stretched copolymer molecules.展开更多
The molecular dynamic simulation of lithium niobate thin films deposited on silicon substrate is carried out by using the dissipative particle dynamics method. The simulation results show that the Si （111） surface i...The molecular dynamic simulation of lithium niobate thin films deposited on silicon substrate is carried out by using the dissipative particle dynamics method. The simulation results show that the Si （111） surface is more suitable for the growth of smooth LiNbO3 thin films compared to the Si（100） surface, and the optimal deposition temperature is around 873 K, which is consistent with the atomic force microscope results. In addition, the calculation molecular number is increased to take the electron spins and other molecular details into account.展开更多
Although phase separation is a ubiquitous phenomenon, the interactions between multiple components make it difficult to accurately model and predict. In recent years, machine learning has been widely used in physics s...Although phase separation is a ubiquitous phenomenon, the interactions between multiple components make it difficult to accurately model and predict. In recent years, machine learning has been widely used in physics simulations. Here,we present a physical information-enhanced graph neural network(PIENet) to simulate and predict the evolution of phase separation. The accuracy of our model in predicting particle positions is improved by 40.3% and 51.77% compared with CNN and SVM respectively. Moreover, we design an order parameter based on local density to measure the evolution of phase separation and analyze the systematic changes with different repulsion coefficients and different Schmidt numbers.The results demonstrate that our model can achieve long-term accurate predictions of order parameters without requiring complex handcrafted features. These results prove that graph neural networks can become new tools and methods for predicting the structure and properties of complex physical systems.展开更多
利用Material Studio 4.3中的颗粒耗散动力学(DPD)方法对可用于乳化炸药的不同结构Gemini表面活性剂在水中的聚集体形态进行了模拟。模拟结果表明:联接基的结构对Gemini表面活性剂聚集体形态影响较大。随着表面活性剂浓度的增加,聚集体...利用Material Studio 4.3中的颗粒耗散动力学(DPD)方法对可用于乳化炸药的不同结构Gemini表面活性剂在水中的聚集体形态进行了模拟。模拟结果表明:联接基的结构对Gemini表面活性剂聚集体形态影响较大。随着表面活性剂浓度的增加,聚集体由球形向棒状再向层状转变;随疏水链长度增加,容易形成大而致密的胶束。联接基的结构对Gemini表面活性剂聚集体形态影响较大。当疏水链较短时,两聚Gemini表面活性剂形成球形胶束的能力高于三聚Gemini表面活性剂;当疏水链较长时,三聚Gemini表面活性剂更易形成棒状胶束,具有更高的形成大胶团的能力。展开更多
基金Project supported by the National Natural Science Foundation of China (Grant Nos. 21074096 and 21104060)
文摘The phase behaviours of a lamellar diblock copolymer/nanorod composite under steady shear are investigated using dissipative particle dynamics. We consider a wide range of nanorod concentrations, where the nanorods each have a preferential affinity to one of the blocks. Our results suggest that shear not only aligns the orientations of the diblock eopolymer templates and nanorods towards flow direction, but also regulates the distribution of the nanorods within the polymer matrix. Meanwhile, the shear-induced reorientation and morphology transitions of the systems also significantly depend on the nanorod concentration. At certain nanorod concentrations, the competitions between shearinduced polymer thinning and nanorods dispersion behaviours determine the phase behaviours of the composites. For high nanorod concentrations, no morphology transition is observed, but reorientation is present, in which the sheared nanorods are arranged into hexagonal packing arrays. Additionally, the orientation behaviour of nanorods is determined directly by the applied shear, also interfered with by the shear-stretched copolymer molecules.
基金supported by the National Basic Research Program of China(Grant No.2011CB922003)the International S&T Cooperation Program of China(Grant No.2013DFG52660)+1 种基金the Taishan Scholar Construction Project Special Fund,Chinathe Fundamental Research Funds for the Central Universities,China(Grant Nos.65030091 and 65010961)
文摘The molecular dynamic simulation of lithium niobate thin films deposited on silicon substrate is carried out by using the dissipative particle dynamics method. The simulation results show that the Si （111） surface is more suitable for the growth of smooth LiNbO3 thin films compared to the Si（100） surface, and the optimal deposition temperature is around 873 K, which is consistent with the atomic force microscope results. In addition, the calculation molecular number is increased to take the electron spins and other molecular details into account.
基金Project supported by the National Natural Science Foundation of China(Grant No.11702289)the Key Core Technology and Generic Technology Research and Development Project of Shanxi Province,China(Grant No.2020XXX013)。
文摘Although phase separation is a ubiquitous phenomenon, the interactions between multiple components make it difficult to accurately model and predict. In recent years, machine learning has been widely used in physics simulations. Here,we present a physical information-enhanced graph neural network(PIENet) to simulate and predict the evolution of phase separation. The accuracy of our model in predicting particle positions is improved by 40.3% and 51.77% compared with CNN and SVM respectively. Moreover, we design an order parameter based on local density to measure the evolution of phase separation and analyze the systematic changes with different repulsion coefficients and different Schmidt numbers.The results demonstrate that our model can achieve long-term accurate predictions of order parameters without requiring complex handcrafted features. These results prove that graph neural networks can become new tools and methods for predicting the structure and properties of complex physical systems.
文摘利用Material Studio 4.3中的颗粒耗散动力学(DPD)方法对可用于乳化炸药的不同结构Gemini表面活性剂在水中的聚集体形态进行了模拟。模拟结果表明:联接基的结构对Gemini表面活性剂聚集体形态影响较大。随着表面活性剂浓度的增加,聚集体由球形向棒状再向层状转变;随疏水链长度增加,容易形成大而致密的胶束。联接基的结构对Gemini表面活性剂聚集体形态影响较大。当疏水链较短时,两聚Gemini表面活性剂形成球形胶束的能力高于三聚Gemini表面活性剂;当疏水链较长时,三聚Gemini表面活性剂更易形成棒状胶束,具有更高的形成大胶团的能力。
