High-accuracy physical property prediction for pure organics via molecular representation learning: bridging data to discovery

The escalating energy crisis has spurred extensive research into organic compounds for energy-efficient 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 transformer-based 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.