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In quantum mechanical elucidations, the molecular energy of formation from the free atoms is an aggregate of cooperativities (synergisms) of orbital interactions. Frequently, such synergisms are deduc...

Proton-coupled electron transfer (PCET) mediated by hydroquinone and related molecules is key to natural and artificial energy conversion. The reactivity of these molecules depends on their bond disso...

Carnegie Mellon chemistry professors, Olexandr Isayev and Gabriel Gomes, are making breakthroughs as part of the NSF Center for Computer Assisted Synthesis.

AQuaRef employs machine learning to refine protein structures from cryo-EM and X-ray data in Phenix. It achieves quantum-level precision, improving model geometry and fit to the data while reducing ov...

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Reaction mechanism search tools have demonstrated the ability to provide insights into likely products and rate-limiting steps of reacting systems. However, reactions involving several concerted bond changes─as can be found in many key steps of natural product syntheses─can complicate the search process. To mitigate these complications, we present a mechanism search strategy particularly suited to help expedite exploration of an exemplary family of such complex reactions, cyclizations. We provide a cost-effective strategy for identifying relevant elementary reaction steps by combining graph-based enumeration schemes and machine learning techniques for intermediate filtering. Key to this approach is our use of a neural network potential (NNP), AIMNet2-rxn, for computational evaluation of each candidate reaction pathway. In this article, we evaluate the NNP’s ability to estimate activation energies, demonstrate the correct anticipation of stereoselectivity, and recapitulate complex enabling steps in natural product synthesis.