Maximizing Machine Learning Interatomic Potential Transferability for the Discovery of the Novel Stellated Octadecagon Bi18-Pt24 Cage Structure

Achieving true transferability remains the central challenge for Machine Learning Interatomic Potentials (ML-IAPs) in modeling complex bimetallic nanoclusters across their vast potential energy surfaces. We systematically investi- gate data selection strategies to optimize the Chebyshev Interaction Model for Efficient Simulation (ChIMES) po- tential for the Bi-Pt nanoclusters by comparing three innovative sampling methods: Principal Component Analysis (PCA)/k-means (structural diversity), t-distributed Stochastic Neighbor Embedding (t-SNE)/k-means (force-space di- versity), and hierarchical clustering. Quantitatively, the PCA/k-means strategy proved most effective for global ac- curacy, yielding the lowest force errors and achieving energy root mean square errors (RMSE) values competitive with DFT, demonstrating excellent accuracy (19.16 meV/atom). Structural validation on 34 unique DFT-optimized isomers further confirmed the potential’s high fidelity, with the best model PCA/k-means reproducing structures with an average root mean square deviation (RMSD) of 0.10 Å. However, the t-SNE methods, by maximizing diversity in the force space, demonstrated superior extrapolative power, leading to the more precise prediction of a novel stel- lated octadecagon Bi18Pt24 cage structure, demonstrating the potential for exploring previously unseen morphologies. Our results establish a clear methodology for strategic data sampling that successfully maximizes ML-IAP transfer- ability, providing an accurate and computationally efficient tool that accelerates the theoretical discovery of complex bimetallic architectures.