  Hybrid quantum/molecular mechanics (QM/MM) models play a pivotal role in
molecular simulations. These models provide a balance between accuracy,
surpassing pure MM models, and computational efficiency, offering advantages
over pure QM models. Adaptive approaches have been developed to further improve
this balance by allowing on-the-fly selection of the QM and MM subsystems as
necessary. We propose a novel and robust adaptive QM/MM method for practical
material defect simulations. To ensure mathematical consistency with the QM
reference model, we employ machine-learning interatomic potentials (MLIPs) as
the MM models. Our adaptive QM/MM method utilizes a residual-based error
estimator that provides both upper and lower bounds for the approximation
error, thus indicating its reliability and efficiency. Furthermore, we
introduce a novel adaptive algorithm capable of anisotropically updating the
QM/MM partitions. This update is based on the proposed residual-based error
estimator and involves solving a free interface motion problem, which is
efficiently achieved using the fast marching method. We demonstrate the
robustness of our approach via numerical tests on a wide range of crystalline
defects.
