Applying machine learning to understand photoprotection: how do triazine-based UV-filters really work?

摘要：紫外线辐射(UVR)对生命有深远的影响，如人类皮肤癌和植物光合作用机制的破坏。这个项目将研究为自然产生的分子提供光保护性能的基本机制，使它们能够吸收紫外线并将其作为热量无害地消散。我们将利用机器学习的势能面的力量来加速基于含时密度泛函理论的电子结构计算，该理论对于动力学来说是准确的，但太慢了。这将使在复杂的溶剂环境中计算激发态寿命等性质成为可能。背景：太阳光谱中的紫外线辐射(UVR)对人类、动物和植物的生活有深远的影响：它当然是众所周知的皮肤癌的原因，虽然阳光是植物光合作用所必需的，但UVR也破坏光合作用机制，增加对入侵病原体的易感性。这项拟议的研究重点是研究为自然产生的分子提供光保护性能的基本机制。启用这种模拟需要大幅加速从头算动力学，这可以通过使用基于DFT数据训练的神经网络的机器学习势能面来实现。华威正在开发的STEEEM代码是一个用于机器学习溶剂中分子激发态势能面的Python程序包：我们可以学习以足够快的从头计算精度执行动力学操作，以生成数百或数千条长的轨迹-至少在量子力学时间尺度(100皮秒)上。我们将使用这些动力学模型通过基于电子结构的动力学模拟来研究UVR与商业防晒霜配方中发现的三氮类紫外线滤光剂的相互作用，以补充时间分辨光谱实验。这一组合是建立这些分子中能量耗散机制的严格结构-动力学-功能图的关键(例如，在Tinosorb S中，酚羟基和三嗪N之间的分子内质子转移)以及它们对基态恢复的保真度，以便它们可以用于多次吸收/恢复循环。这样的研究将使我们能够量化：溶剂；pH；分子结构的改变；以及混合(与润肤剂的混合物)对紫外线滤光片的影响。这也将使我们能够在尽可能接近实际使用的条件下，获得对这些UVF的光化学的前所未有的了解。重要的是，这种“结构-动力学-功能”的方法可能使我们能够为下一代UVF建立“创新设计规则”，以应对配方科学中的工业挑战，例如：(1)增加防晒系数(SPF)，(2)增加关键波长(&gt；370 nm)，(3)在曝光2小时后提高高达90%的光稳定性。

Summary: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.Background: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.