We also implemented a Parser from output CIF to parse the generated output CIF and build the Pymatgen Structure object from it, see Table . The CIF is always formatted as a conventional structure, and the text is parsed line by line. The structure parameters are used to build a Pymatgen structure object with the method from_sites. To assess the quality of the generations, we built Pymatgen crystal structure objects with the API from code and Parser from output CIF using both Text2Struc. Thus, for each original structure, we generated three new structures, see Table : (1) "API, code" by using the API to execute the code generated by the text-code LLM, (2) "API, code+CIF" by using the API to execute the code generated by the text-code-CIF LLM, (3) "CIF parser, code+CIF" by using the CIF parser to build the crystal from CIF generated by the text-code-CIF LLM. Overall, our expectations regarding the quality of structure generations would be that "API, code" would exhibit the strongest performance, "API, code+CIF" less so, and with "CIF parser, code+CIF" as the lowest performing.