  Accessing the synthesizability of crystal structures is pivotal for advancing
the practical application of theoretical material structures designed by
machine learning or high-throughput screening. However, a significant gap
exists between the actual synthesizability and thermodynamic or kinetic
stability, which is commonly used for screening theoretical structures for
experiments. To address this, we develop the Crystal Synthesis Large Language
Models (CSLLM) framework, which includes three LLMs for predicting the
synthesizability, synthesis methods, and precursors. We create a comprehensive
synthesizability dataset including 140,120 crystal structures and develop an
efficient text representation method for crystal structures to fine-tune the
LLMs. The Synthesizability LLM achieves a remarkable 98.6% accuracy,
significantly outperforming traditional synthesizability screening based on
thermodynamic and kinetic stability by 106.1% and 44.5%, respectively. The
Methods LLM achieves a classification accuracy of 91.02%, and the Precursors
LLM has an 80.2% success rate in predicting synthesis precursors. Furthermore,
we develop a user-friendly graphical interface that enables automatic
predictions of synthesizability and precursors from uploaded crystal structure
files. Through these contributions, CSLLM bridges the gap between theoretical
material design and experimental synthesis, paving the way for the rapid
discovery of novel and synthesizable functional materials.
