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

应用机器学习来了解光防护：基于三嗪的紫外线过滤剂如何真正发挥作用？

• 批准号: 2729669
• 金额: --
• 依托单位: University of Warwick
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2729669
• 关键词: Applying; machine; learning; understand; photoprotection

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.

摘要：紫外线辐射 (UVR) 对生命具有深远的影响，例如人类皮肤癌和植物光合机制受损。该项目将研究为天然分子提供光防护特性的基本机制，使它们能够吸收紫外线并将其以热量的形式无害地消散。我们将利用机器学习势能面的力量来加速基于瞬态密度泛函理论的电子结构计算，该理论很准确，但对于动力学来说太慢了。这将能够计算复杂溶剂环境中的激发态寿命等特性。其中一个主要目标将是商业防晒霜配方中的三嗪基紫外线过滤剂。背景：太阳光谱内的紫外线辐射 (UVR) 对人类、动物和植物生命具有深远的影响：众所周知，它是皮肤癌的一个原因，虽然阳光是植物光合作用所必需的，但紫外线也会损害光合作用机器并增加入侵的可能性 病原体。拟议的研究重点是研究为天然存在的分子提供光保护特性的基本机制。实现此类模拟需要从头算动力学的显着加速，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。 Warwick 正在开发的 ESTEEM 代码是一个 Python 包，用于机器学习溶剂中分子激发态的势能表面：我们可以学习以足够快的从头精度执行动力学，以生成数百或数千个长轨迹 - 至少在量子力学时间尺度（数百皮秒）上。我们将使用这些动力学模型来研究 UVR 与基于三嗪的紫外线过滤器的相互作用。 通过基于电子结构的动态模拟补充时间分辨光谱实验来开发商业防晒霜配方。这种组合对于建立这些分子中运行的能量耗散机制的严格结构-动力学-功能图（例如 Tinosorb S 中酚 OH 和三嗪 N 之间的分子内质子转移）及其基态恢复保真度至关重要，以便它们可用于多次吸收/恢复循环。此类研究将使我们能够量化以下效果：溶剂；酸碱度；分子结构的修饰；并在紫外线过滤器上混合（与润肤剂混合）。这也将使我们能够在尽可能接近实际使用条件的情况下，对这些 UVF 的光化学获得前所未有的了解。重要的是，这种“结构-动力学-功能”方法可能使我们能够为下一代 UVF 建立“创新设计规则”，以解决配方科学中的工业挑战，例如：(1) 增加防晒系数 (SPF)，(2) 增加临界波长 (> 370 nm)，以及 (3) 暴露 2 小时后光稳定性提高高达 90%。