Atomistic control of phase boundaries is crucial for optimizing the functional properties of solidsolution ferroelectrics,yet their microstructural mechanisms remain elusive.Here,we harness machine-learning-driven mol...Atomistic control of phase boundaries is crucial for optimizing the functional properties of solidsolution ferroelectrics,yet their microstructural mechanisms remain elusive.Here,we harness machine-learning-driven molecular dynamics to resolve the phase boundary behavior in the KNbO_(3)–KTaO_(3)(KNTO)system.Our simulations reveal that chemical composition and ordering enable precise modulation of polymorphic phase boundaries(PPBs),offering a versatile pathway for materials engineering.Diffused PPBs and polar nano regions,predicted by our model,highly match with experiments,underscoring the fidelity of the machine-learning atomistic simulation.Crucially,we identify elastic and electrostatic mismatches between ferroelectric KNbO_(3)and paraelectric KTaO_(3)as the driving forces behind complex microstructural evolution.This work not only resolves the longstanding microstructural debate but also establishes a generalizable framework for phase boundary engineering toward next-generation high-performance ferroelectrics.展开更多
基金supported by the National Natural Science Foundation of China(Nos.52325204,52421001,W2433118,and U2241243)the Beijing Natural Science Foundation(Nos.IS24026 and JQ22010).
文摘Atomistic control of phase boundaries is crucial for optimizing the functional properties of solidsolution ferroelectrics,yet their microstructural mechanisms remain elusive.Here,we harness machine-learning-driven molecular dynamics to resolve the phase boundary behavior in the KNbO_(3)–KTaO_(3)(KNTO)system.Our simulations reveal that chemical composition and ordering enable precise modulation of polymorphic phase boundaries(PPBs),offering a versatile pathway for materials engineering.Diffused PPBs and polar nano regions,predicted by our model,highly match with experiments,underscoring the fidelity of the machine-learning atomistic simulation.Crucially,we identify elastic and electrostatic mismatches between ferroelectric KNbO_(3)and paraelectric KTaO_(3)as the driving forces behind complex microstructural evolution.This work not only resolves the longstanding microstructural debate but also establishes a generalizable framework for phase boundary engineering toward next-generation high-performance ferroelectrics.
