Automated Experimentation Powers Data Science in Chemistry,Accounts of Chemical Research

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Automated Experimentation Powers Data Science in Chemistry
Accounts of Chemical Research ( IF 16.4 ) Pub Date : 2021-01-20 , DOI: 10.1021/acs.accounts.0c00736
Yao Shi ₁ , Paloma L. Prieto ₁ , Tara Zepel ₁ , Shad Grunert ₁ , Jason E. Hein ₁

Affiliation

Data science has revolutionized chemical research and continues to break down barriers with new interdisciplinary studies. The introduction of computational models and machine learning (ML) algorithms in combination with automation and traditional experimental techniques has enabled scientific advancement across nearly every discipline of chemistry, from materials discovery, to process optimization, to synthesis planning. However, predictive tools powered by data science are only as good as their data sets and, currently, many of the data sets used to train models suffer from several limitations, including being sparse, limited in scope and requiring human curation. Likewise, computational data faces limitations in terms of accurate modeling of nonideal systems and can suffer from low translation fidelity from simulation to real conditions. The lack of diverse data and the need to be able to test it experimentally reduces both the accuracy and scope of the predictive models derived from data science. This Account contextualizes the need for more complex and diverse experimental data and highlights how the seamless integration of robotics, machine learning, and data-rich monitoring techniques can be used to access it with minimal human labor.

中文翻译：

自动化实验助力化学数据科学发展

数据科学彻底改变了化学研究，并继续通过新的跨学科研究打破障碍。结合自动化和传统实验技术的计算模型和机器学习（ML）算法的引入，使从材料发现到工艺优化到合成计划的几乎每个化学学科都取得了科学进步。但是，由数据科学提供支持的预测工具仅与它们的数据集一样好，目前，用于训练模型的许多数据集都受到一些限制，包括稀疏，范围有限以及需要人工管理。同样，计算数据在非理想系统的准确建模方面也面临限制，并且可能会遭受从模拟到实际条件的低转换保真度的问题。缺乏多样化的数据以及能够通过实验进行测试的需求降低了从数据科学衍生的预测模型的准确性和范围。该帐户根据需要提供了更复杂和多样的实验数据，并着重介绍了如何使用机器人技术，机器学习和数据丰富的监视技术的无缝集成来以最少的人力来访问它。

更新日期：2021-02-02

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