In recent years, machine learning has emerged as a powerful tool in materials science, offering the potential to accelerate materials discovery and characterization. In particular, high-throughput materials design and process modeling, which are key driving forces behind the Materials Genome Initiative and the Creating Helpful Incentives to Produce Semiconductors (CHIPS) Act, require a bridge between experiments and multi-scale modeling components, where large language models (LLMs) could play a significant role. Moreover, two recent Nobel Prizes in Physics and Chemistry in 2024 for neural networks and AlphaFold clearly demonstrate the wide applicability of AI/ML in scientific research.