Avagliano Davide, Skreta Marta, Arellano-Rubach Sebastian, Aspuru-Guzik Alán
Department of Chemistry, University of Toronto 80 St. George Street Toronto ON M5S 3H6 Canada
Department of Computer Science, University of Toronto 40 St. George Street Toronto ON M5S 2E4 Canada.
Chem Sci. 2024 Feb 13;15(12):4489-4503. doi: 10.1039/d3sc06440a. eCollection 2024 Mar 20.
Density functional theory (DFT) is the workhorse of computational quantum chemistry. One of its main limitations is that choosing the right functional is a non-trivial task left for human experts. The choice is particularly hard for excited state calculations when using its time-dependent formulation (TD-DFT). This is due to the approximations of the method, but also because the photophysical properties of a molecule are defined by a manifold of states that all need to be properly described. This includes not only the relative energy of the states, but also capturing the correct character, order, and intensity of the transitions. In this work, we developed a neural network to recommend functionals to be used on molecules for TD-DFT calculations, by simultaneously considering all these properties for a manifold of states. This was possible by developing a scoring system to define the accuracy of an excited state's calculation against a higher-accuracy reference. The scoring system is generalizable to any level of theory; we here applied it to evaluate the performance of common functionals of different rungs against a higher accuracy method on a large set of organic molecules. The results are collected in a database that we released and made open, providing four million data points to the community for future applications. The scoring system assigns a value between zero and one hundred to each functional for each molecule, transforming the complicated task of learning photophysical properties into a simpler regression task. We used the dataset to train a graph attention neural network to predict the scores for unseen molecules. We call this oracle DELFI (Data-driven EvaLuation of Functionals by Inference), which can be used to quickly screen and predict the ranking of functionals to calculate the optical properties of organic molecules. We validated DELFI in two experiments: choosing a common functional for a series of spiropyran-merocyanine isomers and a unique functional to screen a large dataset of over 50 000 organic photovoltaic molecules, for which an extensive benchmark would be unfeasible. A corresponding web application allows DELFI to be easily run and the results to be analyzed, alleviating the hurdle of choosing the right functional for TD-DFT calculations.
密度泛函理论（DFT）是计算量子化学的主力方法。其主要局限之一在于，选择合适的泛函对于人类专家而言并非易事。在使用含时形式（TD-DFT）进行激发态计算时，这一选择尤为困难。这不仅是由于该方法的近似性，还因为分子的光物理性质由一系列状态所定义，而所有这些状态都需要得到恰当描述。这不仅包括状态的相对能量，还包括捕捉跃迁的正确特征、顺序和强度。在这项工作中，我们开发了一种神经网络，通过同时考虑一系列状态的所有这些性质，为TD-DFT计算推荐用于分子的泛函。这之所以可行，是因为开发了一种评分系统，以根据更高精度的参考来定义激发态计算的准确性。该评分系统可推广到任何理论水平；我们在此将其应用于评估不同梯级的常见泛函在一大组有机分子上相对于更高精度方法的性能。结果收集在一个我们发布并公开的数据库中，为社区提供了四百万个数据点以供未来应用。评分系统为每个分子的每个泛函赋予一个介于零和一百之间的值，将学习光物理性质的复杂任务转化为一个更简单的回归任务。我们使用该数据集训练了一个图注意力神经网络，以预测未见分子的分数。我们将此预言机称为DELFI（通过推理进行数据驱动的泛函评估），它可用于快速筛选和预测用于计算有机分子光学性质的泛函的排名。我们在两个实验中验证了DELFI：为一系列螺吡喃 - 部花青异构体选择一种常见泛函，以及为一个超过50000个有机光伏分子的大型数据集筛选一种独特泛函，对于后者进行广泛的基准测试是不可行的。一个相应的网络应用程序使DELFI能够轻松运行并分析结果，减轻了为TD-DFT计算选择合适泛函的障碍。
