Numerical Investigation of Tip Shape Classification in Dynamic Atomic Force Microscopy Based on the XGBoost Model: A Simulation-Based Study

Dynamic atomic force microscopy (AFM) is a key technique for nanoscale characterization and mechanical property measurement, where the geometric shape of the probe tip critically determines imaging quality and measurement accuracy. This study proposes a tip shape classification framework based on the dynamic response of the AFM microcantilever. First, a dimensionless dynamic model of the microcantilever is established, and its vibrational response is solved using a finite-difference scheme. For conical, spherical, and flat tip geometries, interaction force models are provided under both non-contact and tapping-mode AFM. Based on these formulations, multidimensional dynamic feature parameters, including amplitude, phase, virial, and root-mean-square force, are extracted. On this basis, an XGBoost-based classifier is constructed for tip shape identification, and the model’s decision-making mechanism is further interpreted through a SHAP-based explainability framework combined with dimensionality reduction and visualization techniques. Results show that, under non-contact conditions, the overall classification accuracy on the test set reaches 96.7%, with a 100% recognition rate for conical tips. Under tapping-mode conditions, the classification accuracies for conical, spherical, and flat tips are 100%, 85.5%, and 98.3%, respectively. The results demonstrate the feasibility of identifying tip shapes from dynamic responses using simulated data, thereby establishing a theoretical and methodological basis for future experimental validation and the development of tip diagnostic techniques.