Artificial Intelligence for Learning Material Synthesis Processes of Thermoelectric Materials

Various calculation-based and data-driven methods have been proposed to discover high-performance thermoelectric materials for sustainable energy resources. However, although several data-driven methods successfully discovered the chemical compositions of promising thermoelectric materials, the practical potential of the existing methods is still limited because there is a complex engineering problem between the discovered materials and the real-world material synthesis. To tackle the engineering problem in material synthesis, we propose a multimodal graph-to-sequence model that predicts necessary synthesis operations and their engineering conditions from chemical compositions of precursor and desired product materials. For an experimental evaluation, we constructed a benchmark dataset containing precursor materials, product materials, and synthesis processes of 771 unique thermoelectric materials. The proposed method achieved prediction accuracy greater than 0.85 in Jaccard similarity and R2-score on the benchmark dataset. Furthermore, the proposed method successfully generated material synthesis recipes described in the human language via large language models.

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Run

• exec_operations.py: Train and evaluate a encoder-decoder model to predict the synthesis operations of the material synthesis recipes.
• exec_eng_conditions.py: Train and evaluate XGBoost-based prediction models to predict the engineering conditions based on the generated sequence embeddings.
• exec_gen_recipe.py: Generate a paragraph descring the material synthesis process based on the predicted synthesis operations and engineering conditions.

Notes

• The save folder contains the pre-trained models and the experimental results in the paper.
• An API key of OpenAI is required to execute exec_gen_recipe.py. Please visit OpenAI_API to get an API key of ChatGPT.