Computational mechanistic studies have emerged as an essential part of many chemistry projects. The information obtained at the atomic level is often essential to improve and expand the experimental results with logical protocols rather than trial-and-error strategies.
In our group, QM methods such as DFT, DLPNO, and MD simulations are used to create energy profiles and to analyze features controlling yield and selectivity. Mechanistic studies can be done for most reactions, including organic synthesis, organocatalysis, photochemistry, and enzymatic catalysis.
Machine learning is changing how the world works. This emerging technology plays an important role in scientific studies, where data-driven algorithms are employed as analytic and predictive tools.
We complement QM with ML techniques to analyze data from reactions and improve the experimental results. Some examples include catalyst design and identifying more selective substrates.