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Machine Learning for Chemical Reactivity: The Importance of Failed Experiments
Felix Strieth-Kalthoff1, Frederik Sandfort1, Marius Kühnemund2
1Westfälische Wilhelms-Universität Münster, Organisch-Chemisches Institut, Corrensstr. 40, 48149, Münster, Germany.
Angewandte Chemie (International Ed. in English)
|May 5, 2022
Summary
Data-driven chemical reaction modeling requires high-quality data, but human biases and errors limit availability. Including "negative" experimental examples and employing data expansion are crucial for accurate predictive models and enhanced data quality in chemistry.
Area of Science:
- Synthetic chemistry and cheminformatics.
Background:
- Quantitative assessment of chemical reactions is vital for synthetic disciplines.
- Data-driven modeling offers potential to streamline reaction optimization.
- Limited availability of high-quality data, due to experimental errors and human biases, hinders predictive modeling.
Purpose of the Study:
- To investigate the impact of human biases on drawing conclusions from chemical reaction data.
- To highlight the importance of "negative" experimental examples.
- To explore data expansion approaches for circumventing data limitations.
Main Methods:
- Series of case studies analyzing chemical reaction data.
- Investigation of biases in experiment selection and result reporting.
- Evaluation of data expansion techniques.
Main Results:
- Human biases significantly impact the interpretation of chemical reaction data.
- "Negative" experimental examples are critical for robust conclusions.
- Data expansion strategies can mitigate limitations caused by data scarcity and bias.
Conclusions:
- Addressing human biases and incorporating negative data are essential for reliable chemical reaction modeling.
- Data expansion approaches offer a pathway to improve data quality and predictive accuracy in chemistry.
- Future work should focus on long-term data quality enhancement strategies.
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