使用化学可转移机器学习势的大光可切换分子的激发态、非绝热动力学
Excited state, non-adiabatic dynamics of large photoswitchable molecules using a chemically transferable machine learning potential
摘要：
光诱导化学过程在自然界中普遍存在，并具有广泛的技术应用。例如，光异构化可以允许具有光可切换支架（诸如偶氮苯）的药物被光激活。原则上，具有所需光物理性质（如高异构化量子产率）的光开关可以通过反应模拟的虚拟筛选来识别。实际上，这些模拟很少用于筛选，因为它们需要数百个轨迹和昂贵的量子化学方法来解释非绝热激发态效应。在这里，我们介绍了一个非绝热人工神经网络（DANN）的基础上非绝热状态，以加速这种模拟偶氮苯衍生物。该网络比用于训练的量子化学方法快六个数量级。DANN可转移到训练集之外的偶氮苯分子，预测与实验相关的看不见的物种的量子产率。我们使用该模型对3,100个假设的分子进行了虚拟筛选，并确定了具有极高预测量子产率的新物种。使用高精度的非绝热动力学模型预测证实。我们的研究结果为快速准确地虚拟筛选光活性化合物铺平了道路。
英文摘要：
Light-induced chemical processes are ubiquitous in nature and have widespread technological applications. For example, photoisomerization can allow a drug with a photo-switchable scaffold such as azobenzene to be activated with light. In principle, photoswitches with desired photophysical properties like high isomerization quantum yields can be identified through virtual screening with reactive simulations. In practice, these simulations are rarely used for screening, since they require hundreds of trajectories and expensive quantum chemical methods to account for non-adiabatic excited state effects. Here we introduce a diabatic artificial neural network (DANN) based on diabatic states to accelerate such simulations for azobenzene derivatives. The network is six orders of magnitude faster than the quantum chemistry method used for training. DANN is transferable to azobenzene molecules outside the training set, predicting quantum yields for unseen species that are correlated with experiment. We use the model to virtually screen 3,100 hypothetical molecules, and identify novel species with extremely high predicted quantum yields. The model predictions are confirmed using high accuracy non-adiabatic dynamics. Our results pave the way for fast and accurate virtual screening of photoactive compounds.
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