AI RESEARCH
Generalization of Long-Range Machine Learning Potentials in Complex Chemical Spaces
arXiv CS.LG
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ArXi:2512.10989v2 Announce Type: replace-cross The vastness of chemical space makes generalization a central challenge in the development of machine learning interatomic potentials (MLIPs). While MLIPs could enable large-scale atomistic simulations with near-quantum accuracy, their usefulness is often limited by poor transferability to out-of-distribution samples.