Polyatomic Complexes: A topologically-informed learning representation for atomistic systems
Machine Learning
2024-09-27 v2 Computational Physics
Abstract
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.
Cite
@article{arxiv.2409.15600,
title = {Polyatomic Complexes: A topologically-informed learning representation for atomistic systems},
author = {Rahul Khorana and Marcus Noack and Jin Qian},
journal= {arXiv preprint arXiv:2409.15600},
year = {2024}
}