Polyatomic Complexes: A topologically-informed learning representation for atomistic systems

• URL: http://arxiv.org/abs/2409.15600v2
• Date: Wed, 25 Sep 2024 18:26:49 GMT
• Title: Polyatomic Complexes: A topologically-informed learning representation for atomistic systems
• Abstract summary: We present a representation of atomistic systems. We provide a general algorithm to encode any atomistic system. We report performance comparable to state-of-the-art methods on numerous tasks.
• Score: 0.5461938536945723
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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