{"ID":2831624,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.10989","arxiv_id":"2512.10989","title":"Generalization of Long-Range Machine Learning Potentials in Complex Chemical Spaces","abstract":"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. Here, we systematically evaluate different MLIP architectures with long-range corrections across diverse chemical spaces and show that such schemes are essential, not only for improving in-distribution performance but, more importantly, for enabling significant gains in transferability to unseen regions of chemical space. To enable a more rigorous benchmarking, we introduce biased train-test splitting strategies, which explicitly test the model performance in significantly different regions of chemical space. Together, our findings highlight the importance of long-range modeling for achieving generalizable MLIPs and provide a framework for diagnosing systematic failures across chemical space. Although we demonstrate our methodology on metal-organic frameworks, it is broadly applicable to other materials, offering insights into the design of more robust and transferable MLIPs.","short_abstract":"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....","url_abs":"https://arxiv.org/abs/2512.10989","url_pdf":"https://arxiv.org/pdf/2512.10989v2","authors":"[\"Michal Sanocki\",\"Julija Zavadlav\"]","published":"2025-12-08T10:32:52Z","proceeding":"physics.chem-ph","tasks":"[\"physics.chem-ph\",\"cs.LG\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}