Proposing a Cartesian-based atomic density expansion as an alternative to spherical harmonics for accurate and efficient interatomic potentials.
This commentary reviews the development and advancements of machine learning interatomic potentials (MLIPs), highlighting the evolution from Gaussian Approximation Potentials (GAP) to Atomic Cluster Expansion (ACE) and its nonlinear extension, Multi-layer ACE (MACE).
Incorporating explicit polarizable long-range interactions into machine learning interatomic potentials significantly enhances their accuracy in predicting material properties and simulating complex phenomena, offering a computationally efficient alternative to ab initio methods.