Jason D. Josephson, John Paul Pezacki, Masaya Nakajima
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引用次数: 0
Abstract
This study leverages machine learning to predict the activation energies of strain-promoted azide-alkyne cycloaddition (SPAAC) reactions. Using DFT calculations, 631 sets of bond angles and Mulliken charges from two acyclic alkynes were collected. Multiple machine learning models were trained on these data, achieving high accuracy (R2 > 0.95). Both bond angle and charge were crucial for reliable predictions. The models successfully predicted activation energies for SPAAC reactions with unseen cycloalkynes, within certain limitations.
期刊介绍：
The Journal of Physical Organic Chemistry is the foremost international journal devoted to the relationship between molecular structure and chemical reactivity in organic systems. It publishes Research Articles, Reviews and Mini Reviews based on research striving to understand the principles governing chemical structures in relation to activity and transformation with physical and mathematical rigor, using results derived from experimental and computational methods. Physical Organic Chemistry is a central and fundamental field with multiple applications in fields such as molecular recognition, supramolecular chemistry, catalysis, photochemistry, biological and material sciences, nanotechnology and surface science.
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