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Computational Photochemistry in the Long Timescale: Sub-ns Photoprocesses in DNA

Periodic Reporting for period 1 - SubNano (Computational Photochemistry in the Long Timescale: Sub-ns Photoprocesses in DNA)

Reporting period: 2019-09-01 to 2021-02-28

Molecular excited states are at the core of a myriad of phenomena. They are crucial for Earth's biosphere, being responsible for the first step in harvesting sunlight through photosynthesis. They are equally vital for vision and bioluminescence. They play a role in mutagenesis and carcinogenesis. In technology, excited states are the basis of optoelectronics, from photovoltaics to light-emitting diodes (LEDs). In the lab, many advanced and routine analytic chemistry tools depend on them.

Complementary to experiments, computational theoretical chemistry has been valuable too understand these phenomena. It allows telling details of how the electronic density is distributed in an excited molecule and how the molecule gets rid of the excess energy. Currently, available methods can successfully predict the excited-state molecular evolution within very short timescales of about a few picoseconds (1 ps corresponds to 10-12 s). Nevertheless, many crucial processes happen in scales significantly longer.

The goal of the SubNano project is to massively speed up dynamics simulations of excited molecules to address phenomena at the timescale of the nanosecond (1 ns = 10-9 seconds), one thousand times longer than the current simulation limits.

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

The SubNano team is developing and implementing 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.

This 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.

All novel methods will be implemented into the Newton-X software and made available for the academic community through new releases of the program.
The Subnano project is designed to be executed in five years. The first phase of Subnano (the first 36 months) is dedicated to the development and test of the new methodology. The second phase (the final 36 months) will be dedicated to applications. There is a 12 months overlap between the phases for knowledge transfer. We are finishing the first half of the first phase, with the following achievements:

* Full redesign of the Newton-X program ( which has been entirely rewritten to increase computational efficience and comply with novel open data standards. The beta version of the program will be released in the Summer 2021.

* Implementation of new machine learning methodologies. One of them, the ML-NEA approach, is already published [1]. We are implementing these methodologies in a interface between Newton-X and MLatom ( ), a software for atomistic machine learning developed by our collaborators in Xiamen. MLatom 2 containing our contributions will be released in the Spring 2021. A development version is already available under request.

* Development of a method for simulating dynamics of molecules excited by incoherent light [2]. It allows predicting processes even longer than the nanonsecond.

* Development of a new method to correct zero-point energy spilling (an artifact of classical approximation when simulating quantum systems). Different from previous approaches, our method does not require pre-calculation of harmonic normal modes, making it much more general. The method is being tested now.

* Development and implementation of three different surface hopping methods without explicit coupling calculation. These methods speed up dynamics, and they are better tailored to be used together with machine learning. The methods are being tested now.

* Evaluation of the quality and performance of machine learning predictions of energies and forces [3,4], spectral predictions [5], velocity adjustment algorithms in surface hopping [6], and surface hopping in the long time scale.

[1] Xue, B. X.; Barbatti, M.; Dral, P. O., J Phys Chem A 2020, 124, 7199-7210.
[2] Barbatti, M., J. Chem. Theory Comput. 2020, 16, 4849-4856.
[3] Pinheiro Jr, M.; Goeal, P.; Ge, F.; Ferré, N.; Dral, P. O.; Barbatti, M., The quest for the best machine learningpotential for molecules. 2020.
[4] Dral, P. O.; Barbatti, M., Nat. Rev. Chem. 2021, accepted.
[5] Bai, S.; Mansour, R.; Stojanović, L.; Toldo, J. M.; Barbatti, M., J. Mol. Model. 2020, 26, 107.
[6] Barbatti, M., J. Chem. Theory Comput. 2021, DOI:10.1021/acs.jctc.1c00012.
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.
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