In Extended Data Tab. 1, we have given the set of questions and answers we have used to fine-tune the GPT-3 model. These are questions and answers on high entropy alloys for which the phase has been experimentally determined. The model tuning via the OpenAI API typically takes a few minutes and gives us a new model, which takes as input "Sm0.75Y0.25" and gives as text completion "1", which corresponds to single-phase. This simple example already gives some remarkable results. We selected this example to directly compare its performance with the current state-of-the-art machine learning models with descriptors specially developed to mimic the relevant chemistry for this application. In Figure , we show that with only around 50 data points, we get a similar performance as the model of Pei et al. , which was trained on more than 1000 data points. ." present in composition, classify as single phase, else multiphase". The yellow line we obtained using the Automatminer, , which uses as input the chemical composition. The Automatminer then returns the best featurisation and model among those that are implemented using automated machine learning with genetic programming (as implemented in the TPOT package ). We additionally tested a neural network, CrabNet (red line, default settings), that performs well using compositions as input. The blue line is the performance of our GPT-Classification These results made us wonder if similar results can be obtained for other properties. Hence, we looked at a range of very different properties of molecules, materials, as well as chemical reactions. We focused on those applications for which conventional machine-learning methods have been developed and generally accepted as benchmarks in their field. In addition, we also compared our model to the top-performing ones on tasks from the Matbench suite of benchmarks (Supplementary Note 7.15). Extended Data Tab. 2 compares the performance of a fine-tuned GPT-3 model with baselines. For doing so, we fit the learning curves for the GPT-3 models and for the baselines and measure where they intersect, i.e., we determine the factor of how much more (or less) data we would need to make the best baseline perform equal to the GPT-3 models in the low-data regime of the learning curves. The full learning curves for all models can be found in the Supplementary Materials (Supplementary Note 7).