SubNano SIGNED

Project "SubNano" data sheet

The following table provides information about the project.

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 Project objective

My goal in the SubNano project is to massively speed up the dynamics simulation of photoexcited molecules to allow addressing sub-nanosecond phenomena (that is, one thousand times above the current limits).

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 transient anion formation in DNA repair.

To fulfill these goals, I 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 constrained to be general (any kind or size of molecule), black-box (minimum user intervention), modular (adaptable to any electronic structure theory), on-the-fly (no need of precomputed potential energy surfaces), and local (independent-trajectories).

It will be implemented into the Newton-X software platform, which I have been the main designer and developer. It will also be made available for all academic community through new releases of Newton-X.

For the last 25 years, theoretical investigations of photodynamical processes have been restricted to the ultrafast (picosecond) regime, selectively choosing problems in this domain. The extension into the sub-ns regime is finally feasible thanks to a large algorithmic infrastructure I have built over the last 13 years, paving the grounds to develop a new research area, atomistic nonadiabatic dynamics on the long timescale.

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