Nevertheless, new advancements may help make progress in this area. The use of natural language processing techniques to parse the scientific literature has resulted in many new structured data sets being compiled from previous literature . New advancements in large language models may allow researchers to extract data sets by simply providing a few examples of structured output from unstructured text . Nevertheless, even though algorithmic improvements are rapid, gaining access to the raw literature data for parsing remains difficult. In parallel, researchers continue to compile data sets outside the materials modeling domain through user contributions . From the analysis side, progress on small data remains more challenging as compared to large data sets. A previous study has generally found a power law scaling of performance with respect to data size for various graph neural network models . Such scaling relies on larger data and is often ineffective for small data sets. Therefore, techniques such as multi-fidelity modeling , hybrid featurization and neural networks , and transfer learning 68 may be needed to leverage big data sets when analyzing small data. While we are likely still far away from true "few shot" learning on general and extrapolative materials science tasks, progress has been reported in using large language models in this manner . It is possible that foundational models, trained on large materials data sets and with general purpose "understanding" of materials science, will be able to serve as the basis for training more targeted machine learning algorithms using smaller data sets.