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Applying Machine Learning to understand photoprotection: how do triazine-based UV-filters really work?

Student: Jacob Eller

Nicholas Hine; Vas Stavros

Ultraviolet radiation (UVR) has far-reaching consequences on life such as skin cancer in humans and damage to photosynthetic machinery in plants. This project will study the fundamental mechanisms that provide naturally-occurring molecules with photoprotective properties, allowing them to absorb UVR and dissipate it harmlessly as heat. We will harness the power of Machine-Learned Potential Energy Surfaces to accelerate electronic structure calculations based on Time- Dependent Density Functional Theory, which are accurate but far too slow for dynamics. This will enable calculations of properties such as excited state lifetimes in a complex solvent environment. One main target of this will be triazine-based UV filters found in commercial sunscreen formulations.

Ultraviolet radiation (UVR) within the solar spectrum has far-reaching consequences on human, animal and plant life: it is of course a well-known cause of skin cancers, and while sunlight is necessary for photosynthesis in plants, UVR also damages photosynthetic machinery and increases susceptibility to invading pathogens. The proposed research focuses on studying the fundamental mechanisms that provide naturally-occurring molecules with photoprotective properties.

Enabling such simulations requires dramatic acceleration of ab initio dynamics, which is possible through the use of Machine-Learned Potential Energy Surfaces based on neural networks trained on DFT data. The ESTEEM code under development at Warwick is a Python package for machine learning of potential energy surfaces of excited states of molecules in solvents: we can learn to perform dynamics with ab initio accuracy fast enough to generate hundreds or thousands of trajectories that are long - at least on quantum mechanical timescales (100s of picoseconds).

We will use these dynamics models to study UVR interaction with triazine-based UV filters found in commercial sunscreen formulations by electronic structure-based dynamical simulations complementing time-resolved spectroscopy experiments. This combination holds the key to establishing a rigorous structure-dynamics-function picture of the energy dissipation mechanisms operating in these molecules (for example intramolecular proton transfer between phenolic OH and triazine N in Tinosorb S) as well as their fidelity for ground state recovery so that they are available for numerous absorption/recovery cycles.

Such studies will allow us to quantify the effects of: solvent; pH; modification of molecular structure; and blend (mixture with emollient) on the UV filters. This will also enable us to garner an unprecedented understanding of the photochemistry of these UVFs, in as-close to real-use conditions as possible. Importantly, such a ‘structure-dynamics-function’ approach may enable us to establish ‘innovative design rules’ for next generation UVFs which tackle industrial challenges in formulation science such as: (1) increased sun protection factor (SPF), (2) increased critical wavelength (> 370 nm), and (3) increased photostability of up to 90% after 2 hours of exposure.