Machine learning interatomic potentials are transforming material science and chemistry by utilizing atomic cluster expansion or message passing frameworks. The proposed Cartesian Atomic Cluster Expansion (CACE) offers a complete set of independent features while integrating low-dimensional embeddings and inter-atomic message passing. CACE demonstrates accuracy, stability, and generalizability across diverse systems like bulk water, small molecules, and high-entropy alloys. The method avoids the complexities of spherical harmonics and Clebsch-Gordan contraction, providing a more efficient approach to symmetrization in Cartesian coordinates. By incorporating message passing mechanisms, CACE enhances the predictive capabilities of the potential model. The framework is stable, scalable, and capable of extrapolating to unseen elements or high temperatures with impressive accuracy.
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