The coarse grained(CG)model implements the molecular dynamics simulation by simplifying atom properties and interaction between them.Despite losing certain detailed information,the CG model is still the first-thought ...The coarse grained(CG)model implements the molecular dynamics simulation by simplifying atom properties and interaction between them.Despite losing certain detailed information,the CG model is still the first-thought option to study the large molecule in long time scale with less computing resource.The deep learning model mainly mimics the human studying process to handle the network input as the image to achieve a good classification and regression result.In this work,the TorchMD,a MD framework combining the CG model and deep learning model,is applied to study the protein folding process.In 3D collective variable(CV)space,the modified find density peaks algorithm is applied to cluster the conformations from the TorchMD CG simulation.The center conformation in different states is searched.And the boundary conformations between clusters are assigned.The string algorithm is applied to study the path between two states,which are compared with the end conformations from all atoms simulations.The result shows that the main phenomenon of protein folding with TorchMD CG model is the same as the all-atom simulations,but with a less simulating time scale.The workflow in this work provides another option to study the protein folding and other relative processes with the deep learning CG model.展开更多
文摘日前光伏发电功率预测是电网经济调度的重要依据。针对K均值(K-means)聚类算法初始聚类中心和聚类数目不易确定的问题和传统神经网络训练参数较多、易陷入局部最优等缺陷,构建了DPK-means和极限学习机(extreme learning machine,ELM)的组合预测算法实现日前光伏发电功率的预测模型。首先,采用密度峰值法(density peaks clustering,DPC)对K-means聚类进行优化,解决了Kmeans算法初始聚类中心和聚类数目不易确定的问题。然后,在利用DPK-means算法对历史气象数据样本聚类分析的基础上,建立ELM预测模型实现日前光伏发电功率的预测。经实测数据验证可知,所提出的组合预测算法可得到较好的预测结果,具有较强的实用性。
基金supported by the National Natural Science Foundation of China(No.31800615 and No.21933010)。
文摘The coarse grained(CG)model implements the molecular dynamics simulation by simplifying atom properties and interaction between them.Despite losing certain detailed information,the CG model is still the first-thought option to study the large molecule in long time scale with less computing resource.The deep learning model mainly mimics the human studying process to handle the network input as the image to achieve a good classification and regression result.In this work,the TorchMD,a MD framework combining the CG model and deep learning model,is applied to study the protein folding process.In 3D collective variable(CV)space,the modified find density peaks algorithm is applied to cluster the conformations from the TorchMD CG simulation.The center conformation in different states is searched.And the boundary conformations between clusters are assigned.The string algorithm is applied to study the path between two states,which are compared with the end conformations from all atoms simulations.The result shows that the main phenomenon of protein folding with TorchMD CG model is the same as the all-atom simulations,but with a less simulating time scale.The workflow in this work provides another option to study the protein folding and other relative processes with the deep learning CG model.
