Polyatomic Complexes: A topologically-informed learning representation for atomistic systems
<|reference_start|>Polyatomic Complexes: A topologically-informed learning representation for atomistic systems: Developing robust representations of chemical structures that enable models to learn topological inductive biases is challenging. In this manuscript, we present a representation of atomistic systems. We begin by proving that our representation satisfies all structural, geometric, efficiency, and generalizability constraints. Afterward, we provide a general algorithm to encode any atomistic system. Finally, we report performance comparable to state-of-the-art methods on numerous tasks. We open-source all code and datasets. The code and data are available at https://github.com/rahulkhorana/PolyatomicComplexes.<|reference_end|>
@article{khorana2024polyatomic,
    title={Polyatomic Complexes: A topologically-informed learning representation
  for atomistic systems},
    author={Rahul Khorana, Marcus Noack, Jin Qian},
    journal={arXiv preprint arXiv:2409.15600},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.15600},
    primaryClass={cs.LG physics.comp-ph}
}