Macarena Tolmos Nehme

Exploring Conformational Transitions of Adenine RNA Dimer via Machine Learning Potentials

This work assesses ML potentials for exploring RNA conformations using the adenine–adenine dinucleoside monophosphate (ApA) dimer, a fundamental RNA building block, and parametrized ML potentials based on the equivariant MACE architecture and informed by both ab initio and semiempirical property data.