In the past few decades, the study of collective motion phase transition process has made great progress. It is also important for the description of the spatial distribution of particles. In this work, we propose a n...In the past few decades, the study of collective motion phase transition process has made great progress. It is also important for the description of the spatial distribution of particles. In this work, we propose a new order parameter φ to quantify the degree of order in the spatial distribution of particles. The results show that the spatial distribution order parameter can effectively describe the transition from a disorderly moving phase to a phase with a coherent motion of the particle distribution and the same conclusion could be obtained for systems with different sizes. Furthermore, we develop a powerful molecular dynamic graph network(MDGNet) model to realize the long-term prediction of the self-propelled collective system solely from the initial particle positions and movement angles. Employing this model, we successfully predict the order parameters of the specified time step. And the model can also be applied to analyze other types of complex systems with local interactions.展开更多
After decades of theoretical studies,the rich phase states of active matter and cluster kinetic processes are still of research interest.How to efficiently calculate the dynamical processes under their complex conditi...After decades of theoretical studies,the rich phase states of active matter and cluster kinetic processes are still of research interest.How to efficiently calculate the dynamical processes under their complex conditions becomes an open problem.Recently,machine learning methods have been proposed to predict the degree of coherence of active matter systems.In this way,the phase transition process of the system is quantified and studied.In this paper,we use graph network as a powerful model to determine the evolution of active matter with variable individual velocities solely based on the initial position and state of the particles.The graph network accurately predicts the order parameters of the system in different scale models with different individual velocities,noise and density to effectively evaluate the effect of diverse condition.Compared with the classical physical deduction method,we demonstrate that graph network prediction is excellent,which could save significantly computing resources and time.In addition to active matter,our method can be applied widely to other large-scale physical systems.展开更多
CuZnAl catalysts with different La loadings were prepared by a complete liquid phase method and tested for higher alcohols（C_（2＋）OH） synthesis from syngas at the conditions of 250–290 ℃, 4.5 MPa, feed low rate ...CuZnAl catalysts with different La loadings were prepared by a complete liquid phase method and tested for higher alcohols（C_（2＋）OH） synthesis from syngas at the conditions of 250–290 ℃, 4.5 MPa, feed low rate = 150 mL/min and H_2/CO = 2. The catalysts were characterized by XRD, H_2-TPR, NH_3-TPD, N_2 adsorption, XPS, and TEM techniques. Characterization results showed that the incorporation of La into CuZnAl catalysts resulted in the decrease of crystallite size of Cu^0 and a strong interaction among copper and zinc or aluminum oxides. Especially, the incorporation of La increased the amount of weak acid and the Cu content on catalyst surface, which was speculated to contribute the formation of higher alcohols. Among these La-promoted CuZnAl catalysts, the La0.1 catalyst exhibited the best catalytic performance and the selectivity to higher alcohols reached above 60% when the reaction temperature was 290 ℃.展开更多
基金the National Natural Science Foundation of China (Grant No. 11702289)Key core technology and generic technology research and development project of Shanxi Province of China (Grant No. 2020XXX013)the National Key Research and Development Project of China。
文摘In the past few decades, the study of collective motion phase transition process has made great progress. It is also important for the description of the spatial distribution of particles. In this work, we propose a new order parameter φ to quantify the degree of order in the spatial distribution of particles. The results show that the spatial distribution order parameter can effectively describe the transition from a disorderly moving phase to a phase with a coherent motion of the particle distribution and the same conclusion could be obtained for systems with different sizes. Furthermore, we develop a powerful molecular dynamic graph network(MDGNet) model to realize the long-term prediction of the self-propelled collective system solely from the initial particle positions and movement angles. Employing this model, we successfully predict the order parameters of the specified time step. And the model can also be applied to analyze other types of complex systems with local interactions.
文摘After decades of theoretical studies,the rich phase states of active matter and cluster kinetic processes are still of research interest.How to efficiently calculate the dynamical processes under their complex conditions becomes an open problem.Recently,machine learning methods have been proposed to predict the degree of coherence of active matter systems.In this way,the phase transition process of the system is quantified and studied.In this paper,we use graph network as a powerful model to determine the evolution of active matter with variable individual velocities solely based on the initial position and state of the particles.The graph network accurately predicts the order parameters of the system in different scale models with different individual velocities,noise and density to effectively evaluate the effect of diverse condition.Compared with the classical physical deduction method,we demonstrate that graph network prediction is excellent,which could save significantly computing resources and time.In addition to active matter,our method can be applied widely to other large-scale physical systems.
基金supported by the Key Program of the National Natural Science Foundation of China(No.21336006)the National Key Technology R&D Program(Grant No.2013BAC14B04)the National Natural Science Foundation of China(No.21176167)
文摘CuZnAl catalysts with different La loadings were prepared by a complete liquid phase method and tested for higher alcohols（C_（2＋）OH） synthesis from syngas at the conditions of 250–290 ℃, 4.5 MPa, feed low rate = 150 mL/min and H_2/CO = 2. The catalysts were characterized by XRD, H_2-TPR, NH_3-TPD, N_2 adsorption, XPS, and TEM techniques. Characterization results showed that the incorporation of La into CuZnAl catalysts resulted in the decrease of crystallite size of Cu^0 and a strong interaction among copper and zinc or aluminum oxides. Especially, the incorporation of La increased the amount of weak acid and the Cu content on catalyst surface, which was speculated to contribute the formation of higher alcohols. Among these La-promoted CuZnAl catalysts, the La0.1 catalyst exhibited the best catalytic performance and the selectivity to higher alcohols reached above 60% when the reaction temperature was 290 ℃.
