In this paper,we propose mesoscience-guided deep learning(MGDL),a deep learning modeling approach guided by mesoscience,to study complex systems.When establishing sample dataset based on the same system evolution data...In this paper,we propose mesoscience-guided deep learning(MGDL),a deep learning modeling approach guided by mesoscience,to study complex systems.When establishing sample dataset based on the same system evolution data,different from the operation of conventional deep learning method,MGDL introduces the treatment of the dominant mechanisms of complex system and interactions between them according to the principle of compromise in competition(CIC)in mesoscience.Mesoscience constraints are then integrated into the loss function to guide the deep learning training.Two methods are proposed for the addition of mesoscience constraints.The physical interpretability of the model-training process is improved by MGDL because guidance and constraints based on physical principles are provided.MGDL was evaluated using a bubbling bed modeling case and compared with traditional techniques.With a much smaller training dataset,the results indicate that mesoscience-constraint-based model training has distinct advantages in terms of convergence stability and prediction accuracy,and it can be widely applied to various neural network configurations.The MGDL approach proposed in this paper is a novel method for utilizing the physical background information during deep learning model training.Further exploration of MGDL will be continued in the future.展开更多
基于反应力场(reactive force field,ReaxFF)的反应分子动力学模拟的结果分析具有挑战性。国际首个ReaxFF MD化学反应分析及可视化工具VARxMD(visulization and analysis of ReaxFF molecular dynamics)可自动生成不同时刻之间完整的化...基于反应力场(reactive force field,ReaxFF)的反应分子动力学模拟的结果分析具有挑战性。国际首个ReaxFF MD化学反应分析及可视化工具VARxMD(visulization and analysis of ReaxFF molecular dynamics)可自动生成不同时刻之间完整的化学反应列表,通过物种检索进一步对反应路径进行分类。但VARxMD目前的反应分析针对的是某一确定条件下单一的ReaxFF MD模拟轨迹,利用VARxMD分析获得一次模拟的完整反应列表需要消耗大量计算资源和时间。本文提出基于数据库来储存VARxMD反应分析结果数据,基于数据库检索进一步分析反应的思路,并采用MVVM(model-view-view model)的系统设计模式、结合渐进式框架Vue.js建立了ReaxFF MD模拟的化学反应数据系统ReaxMDDB(reaction database of ReaxFF MD simulation)。系统应用于多个RP-3模型热解和氧化模拟反应数据的结果表明:该系统不仅实现了多个ReaxFF MD模拟的详细反应的统一分析和化学反应的2D分子结构显示,而且可永久保存模拟获得的反应数据集以备后续进一步分析反应机理。ReaxMDDB具有很好的通用性,为认识不同反应模拟所揭示的共性化学反应机理提供了方便的平台。展开更多
基金supported by the National Natural Science Foundation of China(62050226 and 22078327)the International Partnership Program of Chinese Academy of Sciences(122111KYSB20170068).
文摘In this paper,we propose mesoscience-guided deep learning(MGDL),a deep learning modeling approach guided by mesoscience,to study complex systems.When establishing sample dataset based on the same system evolution data,different from the operation of conventional deep learning method,MGDL introduces the treatment of the dominant mechanisms of complex system and interactions between them according to the principle of compromise in competition(CIC)in mesoscience.Mesoscience constraints are then integrated into the loss function to guide the deep learning training.Two methods are proposed for the addition of mesoscience constraints.The physical interpretability of the model-training process is improved by MGDL because guidance and constraints based on physical principles are provided.MGDL was evaluated using a bubbling bed modeling case and compared with traditional techniques.With a much smaller training dataset,the results indicate that mesoscience-constraint-based model training has distinct advantages in terms of convergence stability and prediction accuracy,and it can be widely applied to various neural network configurations.The MGDL approach proposed in this paper is a novel method for utilizing the physical background information during deep learning model training.Further exploration of MGDL will be continued in the future.
文摘基于反应力场(reactive force field,ReaxFF)的反应分子动力学模拟的结果分析具有挑战性。国际首个ReaxFF MD化学反应分析及可视化工具VARxMD(visulization and analysis of ReaxFF molecular dynamics)可自动生成不同时刻之间完整的化学反应列表,通过物种检索进一步对反应路径进行分类。但VARxMD目前的反应分析针对的是某一确定条件下单一的ReaxFF MD模拟轨迹,利用VARxMD分析获得一次模拟的完整反应列表需要消耗大量计算资源和时间。本文提出基于数据库来储存VARxMD反应分析结果数据,基于数据库检索进一步分析反应的思路,并采用MVVM(model-view-view model)的系统设计模式、结合渐进式框架Vue.js建立了ReaxFF MD模拟的化学反应数据系统ReaxMDDB(reaction database of ReaxFF MD simulation)。系统应用于多个RP-3模型热解和氧化模拟反应数据的结果表明:该系统不仅实现了多个ReaxFF MD模拟的详细反应的统一分析和化学反应的2D分子结构显示,而且可永久保存模拟获得的反应数据集以备后续进一步分析反应机理。ReaxMDDB具有很好的通用性,为认识不同反应模拟所揭示的共性化学反应机理提供了方便的平台。
