The 'Quantum dynamics at conical intersections' (QDYNCI) project aimed to better understand how chemical reactions unfold when nuclear quantum effects such as zero-point energy or non-adiabatic effects are involved. To achieve this, the EU-funded project focused on three simple bimolecular processes. The hydrogen-exchange reaction (H + H2) was a major point of interest due to its small size that allows for high-level calculations and direct comparisons specific to the study. Examining the OH + H2 reaction presented a computational challenge calling for state-of-the-art calculations, especially with regard to arriving at state-to-state reaction probabilities. The two systems present conical intersections. The third process, the H + CH4 reaction, was chosen as the prototypical polyatomic bimolecular reaction. QDYNCI researchers investigated how they could induce resonance states where the wave function is temporarily trapped in the upper cone of the intersection. Termed Slonczewski resonances, these are known to occur in a variety of chemical reactions. The challenge here was to discover the conditions under which they form. Team members hypothesised that by trapping the system in such a state, certain reaction pathways could be enhanced and quantum interference generated, so as to favour particular outcomes of a chemical reaction. The study of chemical reactions has certain major drawbacks. One is that there is exponential growth of the calculation in accordance with dimensionality. Another has to do with the accuracy and availability of potential energy surfaces (PESs). With regard to the first, researchers took a newly developed molecular dynamics approach to compute reaction rates. To overcome the second, QDYNCI project partners came up with a simple solution based on hybrid PESs. Linking two surfaces can be achieved via polynomial switching functions, while transitioning from one description to another is performed on the concept of the trust region. Combining both levels of calculation is a cost-effective means of improving the quality of the description. Achievements in QDYNCI advanced the means by which researchers can better understand the mechanisms at play in selected chemical processes.