Our goal: To transform the way reaction mechanisms are studied

In synthetic chemistry, progress is often hampered by the difficulty of performing meaningful theoretical calculations. We aim to rectify this situation by researching simpler and better computational methods for finding, characterizing, and understanding reaction mechanisms. In pursuit of this goal, the main focus is the development of a practical, real-world technique for predicting reaction mechanisms ex nihilo, based on modern machine learning algorithms combined with tailored quantum mechanical methods. Other efforts include the continued development of both quantitative and interpretive electronic structure methods, at both the density functional and wave function level; these novel methods are distributed in the widely used Molpro quantum chemistry package, of which Knizia is one of the main authors.

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