Large Language Models for Inorganic Synthesis Predictions
Seongmin Kim,
Yousung Jung,
Joshua Schrier
Abstract:We evaluate the effectiveness of pretrained and fine-tuned large language models (LLMs) for predicting the synthesizability of inorganic compounds and the selection of precursors needed to perform inorganic synthesis. The predictions of fine-tuned LLMs are comparable to�and sometimes better than�recent bespoke machine learning models for these tasks but require only minimal user expertise, cost, and time to develop. Therefore, this strategy can serve both as an effective and strong baseline for future machine … Show more
Large language models (LLMs) allow for the extraction of structured data from unstructured sources, such as scientific papers, with unprecedented accuracy and performance.
Large language models (LLMs) allow for the extraction of structured data from unstructured sources, such as scientific papers, with unprecedented accuracy and performance.
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