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<p>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 <i>R</i><sup>2</sup> 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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月11日

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  • Qi Ou,
  • Hongshuai Wang,
  • Minyang Zhuang,
  • Shangqian Chen,
  • Lele Liu,
  • Ning Wang,
  • Zhifeng Gao

摘要

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.