Treffer: Investigators from University of Calgary Have Reported New Data on Machine Learning (A Problem-based Introduction To Machine Learning In the Undergraduate Organic Chemistry Laboratory: Prediction of Diels-alder Reaction Rates).

The article focuses on a research initiative at the University of Calgary that developed an inquiry-based dry laboratory experience for upper-year undergraduate students, integrating machine learning into organic chemistry. Students created datasets and utilized multivariate linear regression in Python to predict energy barriers in Diels-Alder reactions, while also engaging in simulated drug development problems. Feedback from students indicated increased engagement in critical thinking, collaboration, and a greater appreciation for computational tools in chemistry. The research emphasized the importance of a growth mindset, allowing students to learn from mistakes and reflect on their experiences. [Extracted from the article]

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