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
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.
