arXiv:2512.16882v2 Announce Type: replace-cross Abstract: Machine learning interatomic potentials (MLIPs) have brought substantial gains in the extrapolation capability in computational chemistry. However, most equivariant models are typically built with spherical tensors (STs), while Cartesian tensor formulations remain less developed despite their natural alignment with atomic coordinates and tensorial targets. In this work, we develop a Cartesian framework for irreducible Cartesian tensors (ICTs) by introduce the \texttt{Cartesian-3j} symbol and Cartesian Generalized Clebsch-Gordan Coeffici
The continuous drive for more accurate and efficient computational chemistry methods, coupled with advancements in machine learning, makes this a timely development.
Improving MLIPs through better mathematical frameworks can lead to more precise material science simulations, accelerating discovery and design in various fields.
The development of a Cartesian framework for irreducible Cartesian tensors offers an alternative and potentially more aligned approach for building equivariant MLIPs compared to traditional spherical tensor methods.
- · Computational Chemists
- · Material Scientists
- · AI/ML Researchers in Physics
- · Pharmaceutical Industry
More accurate and efficient simulations of atomic interactions for materials design and drug discovery.
Reduced time and cost in developing new materials with desired properties.
Acceleration of innovation in sectors reliant on new material breakthroughs, such as sustainable energy or advanced manufacturing.
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Read at arXiv cs.LG