Amorphous and non-stoichiometric hafnium oxide(a-HfO_(x))systems are essential for advanced electronic applications due to their superior electrical properties.Simulating their atomic behaviors under electric fields(E...Amorphous and non-stoichiometric hafnium oxide(a-HfO_(x))systems are essential for advanced electronic applications due to their superior electrical properties.Simulating their atomic behaviors under electric fields(Efield)is critical but challenging.Ab-initio molecular dynamics(AIMD)offer high accuracy but is computationally expensive,while classical MD lacks precision.To address this,we develop a charge equilibration integrated graph neural network(CIGNN)model that predicts atomic charge,energy,and force under Efield conditions.Using the CIGNN model and AIMD datasets,we develop a CIGNN-based machine learning potential(CNMP)optimized for a-HfO_(x)systems.The CNMP achieves quantum mechanical accuracy and effectively captures the atomic behaviors and dynamic properties of these systems across varying temperatures,densities,and E_(field)conditions.We expect the CNMP to serve as a valuable tool for studying field-induced phenomena in complex systems and to provide a foundation for advancing innovations in electronic applications.展开更多
基金supported by the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning No. NRF-2020R1A6C101A202 and NRF-2024M3A7C2045166 and NRF-2021M3I3A1084940 and RS-2023-00257666 and RS-2024-00446683 and RS-2024-00450836.
文摘Amorphous and non-stoichiometric hafnium oxide(a-HfO_(x))systems are essential for advanced electronic applications due to their superior electrical properties.Simulating their atomic behaviors under electric fields(Efield)is critical but challenging.Ab-initio molecular dynamics(AIMD)offer high accuracy but is computationally expensive,while classical MD lacks precision.To address this,we develop a charge equilibration integrated graph neural network(CIGNN)model that predicts atomic charge,energy,and force under Efield conditions.Using the CIGNN model and AIMD datasets,we develop a CIGNN-based machine learning potential(CNMP)optimized for a-HfO_(x)systems.The CNMP achieves quantum mechanical accuracy and effectively captures the atomic behaviors and dynamic properties of these systems across varying temperatures,densities,and E_(field)conditions.We expect the CNMP to serve as a valuable tool for studying field-induced phenomena in complex systems and to provide a foundation for advancing innovations in electronic applications.
