Topology-Driven Search for Molecular Links in Nanoclusters: Structure Prediction for Au₁₆(SR)₁₄

Molecular topology provides a fundamental understanding of the molecular structures and dictates the chemical properties. Here, knot theory, a branch of mathematics, is leveraged to systematically search for molecular links with machine learning interatomic potential and give a structure prediction for Au₁₆(SR)₁₄ (SR denotes a thiolate ligand). This knot theory-based search method is also applicable to a variety of low-valency nanoclusters, including the widely studied Au₁₅(SR)₁₃. The thoroughly searched energy landscape of the small sized nanoclusters also suggests potential isoelectronic conversions during the typical nanocluster reduction growth. This study not only provides a possible scaffold for future Au nanocluster structure prediction but also promotes the understanding of the implications of twists and turns for a nanocluster in a topological space.