  Crystal structure prediction (CSP) stands as a powerful tool in materials
science, driving the discovery and design of innovative materials. However,
existing CSP methods heavily rely on formation enthalpies derived from density
functional theory (DFT) calculations, often overlooking differences between DFT
and experimental values. Moreover, material synthesis is intricately influenced
by factors such as kinetics and experimental conditions. To overcome these
limitations, a novel collaborative approach was proposed for CSP that combines
DFT with experimental data, utilizing advanced deep learning models and
optimization algorithms. We illustrate the capability to predict formation
enthalpies that closely align with actual experimental observations through the
transfer learning on experimental data. By incorporating experimental
synthesizable information of crystals, our model is capable of reverse
engineering crystal structures that can be synthesized in experiments. Applying
the model to 17 representative compounds, the results indicate that the model
can accurately identify experimentally synthesized structures with high
precision. Moreover, the obtained formation enthalpies and lattice constants
closely align with experimental values, underscoring the model's effectiveness.
The synergistic approach between theoretical and experimental data bridges the
longstanding disparities between theoretical predictions and experimental
results, thereby alleviating the demand for extensive and costly experimental
trials.
