Following publication of the original article[1],the statement of Data availability and Competing interests have been added.Data availability The datasets used and analyzed during this study are available from the cor...Following publication of the original article[1],the statement of Data availability and Competing interests have been added.Data availability The datasets used and analyzed during this study are available from the corresponding author upon reasonable request.展开更多
Accurate prediction of molecular polarizability is essential for understanding electrical,optical,and dielectric properties of materials.Traditional quantum mechanical(QM)methods,though precise for small systems,are c...Accurate prediction of molecular polarizability is essential for understanding electrical,optical,and dielectric properties of materials.Traditional quantum mechanical(QM)methods,though precise for small systems,are computationally prohibitive for large-scale systems.In this work,we proposed an efficient approach for calculating molecular polarizability of condensed-phase systemsby embedding atomic polarizability constraints into the tensorial neuroevolution potential(TNEP)framework.Using n-heneicosane as a benchmark,a training data set was constructed frommolecular clusters truncated from the bulk systems.Atomic polarizabilities derived from semi-empirical QM calculations were integrated as training constraints for its balance of computational efficiency and physical interpretability.The constrained TNEP model demonstrated improved accuracy in predicting molecular polarizabilities for larger clusters and condensed-phase systems,attributed to the model’s refined ability to properly partition molecular polarizabilities into atomic contributions across systems with diverse configurational features.Results highlight the potential of the TNEP model with atomic polarizability constraint as a generalizable strategy to enhance the scalability and transferability of other atom-centered machine learning-based polarizability models,offering a promising solution for simulating large-scale systems with high data efficiency.展开更多
An approach of transmission network expan-sion planning with embedded constraints of short circuit currents and N-1 security is proposed in this paper.The problem brought on by the strong nonlinearity property of shor...An approach of transmission network expan-sion planning with embedded constraints of short circuit currents and N-1 security is proposed in this paper.The problem brought on by the strong nonlinearity property of short circuit currents is solved with a linearization method based on the DC power flow.The model can be converted to a mixed-integer linear programming problem,realizing the optimization of planning model that considers the constraints of linearized short circuit currents and N-1 security.To compensate the error caused by the assump-tions of DC power flow,the compensation factor is pro-posed.With this factor,an iterative algorithm that can compensate the linearization error is then presented.The case study based on the IEEE 118-bus system shows that the proposed model and approach can be utilized to:opti-mize the construction strategy of transmission lines;ensure the N-1 security of the network;and effectively limit the short circuit currents of the system.展开更多
文摘Following publication of the original article[1],the statement of Data availability and Competing interests have been added.Data availability The datasets used and analyzed during this study are available from the corresponding author upon reasonable request.
基金supported by “Pioneer” and “Leading Goose” R&D Program of Zhejiang (grant number: 2023C01182)the National Natural Science Foundation of China (grant numbers: 22408314, 22178299, and 51933009)Nan Xu would like to thank the financial support provided by the Startup Funds of the Institute of Zhejiang University-Quzhou.
文摘Accurate prediction of molecular polarizability is essential for understanding electrical,optical,and dielectric properties of materials.Traditional quantum mechanical(QM)methods,though precise for small systems,are computationally prohibitive for large-scale systems.In this work,we proposed an efficient approach for calculating molecular polarizability of condensed-phase systemsby embedding atomic polarizability constraints into the tensorial neuroevolution potential(TNEP)framework.Using n-heneicosane as a benchmark,a training data set was constructed frommolecular clusters truncated from the bulk systems.Atomic polarizabilities derived from semi-empirical QM calculations were integrated as training constraints for its balance of computational efficiency and physical interpretability.The constrained TNEP model demonstrated improved accuracy in predicting molecular polarizabilities for larger clusters and condensed-phase systems,attributed to the model’s refined ability to properly partition molecular polarizabilities into atomic contributions across systems with diverse configurational features.Results highlight the potential of the TNEP model with atomic polarizability constraint as a generalizable strategy to enhance the scalability and transferability of other atom-centered machine learning-based polarizability models,offering a promising solution for simulating large-scale systems with high data efficiency.
基金This work was supported by National Key Technology R&D Program of China(No.2013BAA01B02)National Natural Science Foundation of China(Nos.51325702,51407100).
文摘An approach of transmission network expan-sion planning with embedded constraints of short circuit currents and N-1 security is proposed in this paper.The problem brought on by the strong nonlinearity property of short circuit currents is solved with a linearization method based on the DC power flow.The model can be converted to a mixed-integer linear programming problem,realizing the optimization of planning model that considers the constraints of linearized short circuit currents and N-1 security.To compensate the error caused by the assump-tions of DC power flow,the compensation factor is pro-posed.With this factor,an iterative algorithm that can compensate the linearization error is then presented.The case study based on the IEEE 118-bus system shows that the proposed model and approach can be utilized to:opti-mize the construction strategy of transmission lines;ensure the N-1 security of the network;and effectively limit the short circuit currents of the system.
