Algorithms for Transitions: From Catalysis to Spintronics 2027

Traversing the configurational space of molecules and their associated potential energy surfaces underpins most application areas in physical chemistry. For generations, this domain has represented an ever-expanding horizon for theoretical advances. To this end, atomistic simulations utilizing sophisticated potential energy surfaces provide the necessary framework to investigate complex transition states and rare events. This conference collects practitioners and pioneers to better address recent advances and paradigm shifts in the field, including the rise of exa-scale compute resources, machine learning potentials, and large language (generative) models.