Chemical engineers heavily rely on precise knowledge of physicochemical properties to model chemical processes. Despite the growing popularity of deep learning, it is only rarely applied for property prediction due to data scarcity and limited accuracy for compounds in industrially-relevant areas of the chemical space. Herein, we present a geometric deep learning framework for predicting gas- and liquid-phase properties based on novel quantum chemical datasets comprising 124,000 molecules. Our findings reveal that the necessity for quantum-chemical information in deep learning models varies significantly depending on the modeled physicochemical property. Specifically, our top-performing geometric model meets the most stringent criteria for “chemically accurate” thermochemistry predictions. We also show that by carefully selecting the appropriate model featurization and evaluating prediction uncertainties, the reliability of the predictions can be strongly enhanced. These insights represent a crucial step towards establishing deep learning as the standard property prediction workflow in both industry and academia. Scientific contribution We propose a flexible property prediction tool that can handle two-dimensional and three-dimensional molecular information. A thermochemistry prediction methodology that achieves high-level quantum chemistry accuracy for a broad application range is presented. Trained deep learning models and large novel molecular databases of real-world molecules are provided to offer a directly usable and fast property prediction solution to practitioners.

中文翻译：

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几何深度学习用于跨化学空间的分子特性预测，具有化学准确性


化学工程师严重依赖物理化学性质的精确知识来模拟化学过程。尽管深度学习越来越受欢迎，但由于数据稀缺以及化学领域工业相关领域化合物的准确性有限，它很少应用于性质预测。在此，我们提出了一个几何深度学习框架，用于基于包含 124,000 个分子的新型量子化学数据集来预测气相和液相特性。我们的研究结果表明，深度学习模型中量子化学信息的必要性根据建模的物理化学性质的不同而有很大差异。具体来说，我们性能最佳的几何模型满足“化学准确”热化学预测的最严格标准。我们还表明，通过仔细选择适当的模型特征并评估预测不确定性，可以大大提高预测的可靠性。这些见解代表了将深度学习建立为工业界和学术界标准财产预测工作流程的关键一步。科学贡献我们提出了一种灵活的属性预测工具，可以处理二维和三维分子信息。提出了一种热化学预测方法，可在广泛的应用范围内实现高水平的量子化学精度。提供训练有素的深度学习模型和现实世界分子的大型新颖分子数据库，为从业者提供直接可用且快速的性质预测解决方案。