For example, a study explored the use of state-of-the-art pre-trained general-purpose and domainspecific language models to extract knowledge triples for MOFs . The authors constructed a knowledge graph benchmark with 7 relations for 1248 published MOF synonyms. Experimental probing revealed that such domain-specific pre-trained language models (PLMs) outperformed general-purpose PLMs for predicting MOF related triples. The authors also conceded from their overall benchmarking results that the use of PLMs alone to create domain-specific knowledge graphs is still far from being practical and requires the development of better-informed PLMs for specific materials design tasks. The group of Olivetti used LLMs to generate knowledge graphs (MatKG2) for the entire domain of materials science, taking ontological information into account as opposed to using statistical co-occurrence alone . Zhao et al. ( ) used fine-tuned Bidirectional Encoder Representations from a Transformer (BERT) model and tested it with respect to data extraction from published corpora. They reported that the model achieved an impressive F-score of 85% for the task of materials named entity recognition. The F-score is a metric used to evaluate the accuracy of a model in binary classification tasks. Sasidhar et al. ( ) integrated natural language processing and deep learning for the design of corrosion-resistant alloys . They also highlighted the general challenges in utilizing textual data in machine learning models for material datasets and proposed an automated approach to transform language data into a format suitable for subsequent deep neural network processing. This method significantly improved the accuracy of pitting potential predictions for alloys, providing insights into the critical descriptors for alloy resistance, like configurational entropy and atomic packing efficiency. Pei et al. proposed a concept of 'context similarity' to select chemical elements with high mutual solubility for discovering high-entropy alloys. They trained a word-embedding language model with the abstracts of 6.4 million papers to calculate the 'context similarity'. With this approach they designed a workflow to design lightweight high-entropy alloys, which suggested even 6-and 7-component lightweight high-entropy alloys by finding nearly 500 promising alloys out of 2.6 million candidates.