We then followed the official training and testing procedure of Matbench to evaluate the different input formats, as illustrated in Fig. . The overall performance on structural tasks (using both CIF and NL inputs) is better compared to composition tasks, with all results achieving over 80% of the best values. The results demonstrate that the natural language transformation is effective, with an average improvement of 40.3%. Therefore, we use the NL input results to compare with other models on the matbench leaderboard, as shown in Fig. , and submit our model under the name AlchemBERT. and "RAW" refer to the original formats used in matbench for structure and composition tasks, respectively, while "NL" represents the transformed natural language format generated by us. The percentile values in the figure represent the performance differences for each task, with positive values indicating that the natural language (NL) input outperforms the raw input.