Un descriptor físicamente interpretable para predecir y diseñar nanomateriales bidimensionales mecánicamente...

The mechanical exfoliability of two-dimensional (2D) nanomaterials is fundamentally governed by the exfoliation energy (E_ex). Conventional strategies for determining E_ex mainly depend on either indirect experimental approaches or computationally demanding first-principles calculation, which impose significant demands on time and computational resources. Data-driven methods provide an efficient alternative, yet the limited interpretability of machine learning predictions often restricts their applicability. Herein, we construct a physically interpretable descriptor P₄₃, by coupling a CatBoost model (RMSE = 57.83 meV/atom) with the Sure Independence Screening and Sparsifying Operator (SISSO) for predicting and designing mechanically exfoliable 2D nanomaterials. Remarkably, P₄₃ achieves a recall rate of 99.0% in identifying mechanically exfoliable 2D nanomaterials using four concise parameters. Further analysis of its electronic (φ) and geometric (θ) contributions reveals that φ and θ play dominant roles in predicting low-E_ex (<150 meV/atom) and high-E_ex (>350 meV/atom) 2D nanomaterials, respectively. Leveraging P₄₃ within the element substitution method (ESM) framework, we screened A- and B-site combinations and discovered 8946 novel AB-type 2D structures, including 111 candidates confirmed as exfoliable in the 2DMatPedia database. Overall, this study provides new insights into the E_ex, highlighting the significant role of P₄₃ in accelerating ESM for the development of novel 2D nanomaterials.