A Physically Interpretable Descriptor for Predicting and Designing Mechanically Exfoliable Two-Dimensional Nanomaterials

The mechanical exfoliability of two-dimensional (2D)
nanomaterials
is fundamentally governed by the exfoliation energy (Eex). Conventional strategies for determining Eex 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-Eex (<150 meV/atom) and high-Eex (>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 Eex, highlighting the significant
role of P43 in accelerating ESM for the
development of novel 2D nanomaterials.