Chemical engineering is being rapidly transformed by the tools of data science. On the horizon, artificial intelligence (AI) applications will impact a huge swath of our work, ranging from the discovery and design of new molecules to operations and manufacturing and many areas in between. Early adoption of data science, machine learning, and early examples of AI in chemical engineering has been rich with examples of molecular data science-the application tools for molecular discovery and property optimization at the atomic scale. We summarize key advances in this nascent subfield while introducing molecular data science for a broad chemical engineering readership. We introduce the field through the concept of a molecular data science life cycle and discuss relevant aspects of five distinct phases of this process: creation of curated data sets, molecular representations, data-driven property prediction, generation of new molecules, and feasibility and synthesizability considerations. Expected final online publication date for the Annual Review of Chemical and Biomolecular Engineering, Volume 12 is June 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

中文翻译：

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化学工程中的数据科学：分子科学中的应用。

数据科学工具正在迅速改变化学工程。在不久的将来，人工智能（AI）应用将影响我们的工作范围，从发现和设计新分子到运营和制造以及介于两者之间的许多领域。数据科学，机器学习和化学工程学中AI的早期实例的早期采用已丰富了分子数据科学的实例-分子级分子发现和性能优化的应用工具。我们总结了这个新兴子领域的主要进展，同时为广泛的化学工程读者介绍了分子数据科学。我们通过分子数据科学生命周期的概念介绍该领域，并讨论该过程五个不同阶段的相关方面：创建精选数据集，分子表示，数据驱动的性质预测，新分子的产生以及可行性和可合成性方面的考虑。《化学和生物分子工程年度评论》第12卷的最终最终在线发布日期为2021年6月。有关修订的估算，请参见http://www.annualreviews.org/page/journal/pubdates。