MolCrysKit: A Topology-Aware Toolkit for Bridging Experimental Molecular-Crystal Structures and Simulation-Ready Modeling

Modeling molecular crystals requires addressing two main challenges: (i) maintaining the integrity of chemically bonded molecular units during structural operations and (ii) translating experimental crystallographic data, which often include disorder and missing hydrogen atoms, into fully resolved, simulation-ready models. Existing materials modeling toolkits are largely atom-centric and thus face limitations for molecular crystals, where chemically bonded entities must be treated as rigid topological units within a periodic lattice. Here we present MolCrysKit, a Python toolkit designed to automate topology-aware conversion and manipulation of experimental crystallographic information files (CIFs) into computationally viable molecular crystal models. MolCrysKit implements three complementary strategies: (i) a graph-theoretic approach for resolving disorder, (ii) a topology-preserving algorithm for surface slab generation, and (iii) a chemical-environment-aware scheme for reconstructing missing hydrogen atoms. The toolkit provides an interface that supports integration with higher-level automated workflows, enabling reproducible transformations of incomplete or ambiguous crystallographic data into simulation-ready models. Our design enables artificial intelligence (AI)-driven pipelines, particularly those utilizing large language model (LLM) agents, to reliably construct chemically consistent simulation systems from crystallographically ambiguous or structurally incomplete experimental crystal structures.