用分子机器学习加速新分子与化工流程设计
Molecular Machine Learning in Chemical Process Design
- 结合图神经网络与物理知识构建更准确的分子预测模型
- 可实现纯组分及混合物性质的高精度预测
- 适合化工研发人员和算法工程师参考
本文展望了分子机器学习在化学过程工程中的应用前景。近年来，分子机器学习在（i）精确预测纯组分及其混合物的性质，以及（ii）探索新型分子结构的化学空间方面展现出巨大潜力。我们综述了当前最先进的分子机器学习模型，并探讨了未来研究方向，包括通过融合物理化学知识，进一步提升图神经网络和Transformer等方法的性能。此外，文章讨论了将分子机器学习拓展至化学过程层面的可行性，这一方向虽极具吸引力但尚待深入探索。通过将分子机器学习融入过程设计与优化框架，有望显著加快新型分子与工艺的发现进程。为此，亟需建立分子与过程设计的基准测试体系，并与化工产业合作进行实际验证。
原文摘要 · Abstract (English)
We present a perspective on molecular machine learning (ML) in the field of chemical process engineering. Recently, molecular ML has demonstrated great potential in (i) providing highly accurate predictions for properties of pure components and their mixtures, and (ii) exploring the chemical space for new molecular structures. We review current state-of-the-art molecular ML models and discuss research directions that promise further advancements. This includes ML methods, such as graph neural networks and transformers, which can be further advanced through the incorporation of physicochemical knowledge in a hybrid or physics-informed fashion. Then, we consider leveraging molecular ML at the chemical process scale, which is highly desirable yet rather unexplored. We discuss how molecular ML can be integrated into process design and optimization formulations, promising to accelerate the identification of novel molecules and processes. To this end, it will be essential to create molecule and process design benchmarks and practically validate proposed candidates, possibly in collaboration with the chemical industry.
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