Hybrid quantum/molecular mechanics models (QM/MM methods) are widely used in
material and molecular simulations when pure MM models cannot ensure adequate
accuracy but pure QM models are computationally prohibitive. Adaptive QM/MM
coupling methods feature on-the-fly classification of atoms, allowing the QM
and MM subsystems to be updated as needed. The state-of-art "machine-learned
interatomic potentials (MLIPs)" can be applied as the MM models for consistent
QM/MM methods with rigorously justified accuracy. In this work, we propose a
robust adaptive QM/MM method for practical material defect simulation, which is
based on a developed residual-based error estimator. The error estimator
provides both upper and lower bounds for the approximation error, demonstrating
its reliability and efficiency. In particular, we introduce three minor
approximations such that the error estimator can be evaluated efficiently
without losing much accuracy. To update the QM/MM partitions anisotropically, a
novel adaptive algorithm is proposed, where a free interface motion problem
based on the proposed error estimator is solved by employing the fast marching
method. We implement and validate the robustness of the adaptive algorithm on
numerical simulations for various complex crystalline defects