Machine Learning for Chemical Reactivity: The Importance of Failed Experiments

Assessing the outcomes of chemical reactions in a quantitative fashion has been a cornerstone across all synthetic disciplines. Classically approached through empirical optimization, data-driven modelling bears an enormous potential to streamline this process. However, such predictive models require significant quantities of high-quality data, the availability of which is limited: Main reasons for this include experimental errors and, importantly, human biases regarding experiment selection and result reporting. In a series of case studies, we investigate the impact of these biases for drawing general conclusions from chemical reaction data, revealing the utmost importance of negative examples. Eventually, case studies into data expansion approaches showcase directions to circumvent these limitations-and demonstrate perspectives towards a long-term data quality enhancement in chemistry.

Automated summary

The article examines the impact of biases in chemical reaction data on drawing general conclusions and highlights the importance of negative examples. The research showcases the potential of data expansion methods to address these limitations and demonstrates future prospects for improving data quality in the field of chemistry.