摘要
The escalating energy crisis has spurred extensive research into organic compounds for energyefficient applications,taking advantage of their environmental friendliness,cost-effective synthesis,and adaptable molecular structures.Traditional trial-and-error methods for discovering highly functional organic compounds are expensive and time-consuming.We employed a 3D transformerbased molecular representation learning algorithm to create the Org-Mol pre-trained model,using 60 million semi-empirically optimized small organic molecule structures.After fine-tuning with public experimental data,the model can accurately predict various physical properties of pure organics,with test set R2 values exceeding 0.92.These fine-tuned models are used in high-throughput screening among millions of ester molecules to identify novel immersion coolants,resulting in the experimental validation of two promising candidates.This work not only demonstrates the potential of Org-Mol in predicting bulk properties for pure organic compounds but also paves the way for the rational and efficient development of ideal candidates for energy-saving materials.
基金
supported by research grants from China Petroleum&Chemical Corp(funding number 124014)
the financial support from the National Key R&D Program of China(Grant No.2024YFA1510200).
