Generalization of Long-Range Machine Learning Potentials in Complex Chemical Spaces
This addresses the problem of limited usefulness of MLIPs for large-scale atomistic simulations in materials science, offering incremental improvements in transferability.
The study tackled the challenge of poor transferability in machine learning interatomic potentials (MLIPs) by evaluating architectures with long-range corrections, showing they enable significant gains in generalization to unseen chemical spaces, though no concrete numbers were provided.
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.