What is SubNano?

SubNano is a research project funded by an ERC Advanced Grant (2019 – 2024).

The goal of SubNano is to massively speed up dynamics simulations of photoexcited molecules to address sub-nanosecond phenomena (that is, one thousand times above the current simulation limits).

Such methods will allow exploring phenomena, like vibrational relaxation, fluorescence, slow internal conversion, intersystem crossing, which have been left aside by computational chemistry, too focused on ultrafast processes.

The sub-ns methodology will be employed to investigate the long timescale nonadiabatic dynamics of photoinduced processes in nucleic acids, including DNA photostabilization via excitonic processes, biological fluorescent markers, and DNA pyrimidine-dimer repair.

How is SubNano developed?

The SubNano team will develop and implement a series of methods to extend nonadiabatic dynamics simulations into the new timescale, mainly based on a novel adaptive diabatic machine learning algorithm, and a novel zero-point-corrected and vibronically-corrected mixed quantum-classical method.

The sub-ns methodology will be implemented into Newton-X and made available for the academic community through new releases of the program.

The success of the SubNano project will have an enormous impact on the research field, allowing us to investigate outstanding interdisciplinary phenomena in chemistry, biology, and technology, which have been neglected due to a lack of methods.

Who is in SubNano?

SubNano is led by Mario Barbatti, professor at the University of Aix Marseille, in Marseille, France.

The SubNano team is composed of an IT engineer, postdocs, and doctoral students, all committed to the development and application of the SubNano methods.