• Automating analysis. An important use case of computation in chemistry is to concisely present abstract concepts (for example, visualizing electron density to learn about covalent bonding). However, calculating these properties would normally require users develop significant familiarity with electronic structure theory and quantum chemistry packages. This separation in domain knowledge has led to reluctance for experimentalists to apply computations during the design of experiments or might not know what the feasible and and most efficient calculations are. LLMs provide a natural solution to bridge this gap, by helping experimentalists automate more advanced computational analysis and visualization to fulfill the daily needs of simulation.