低成本机器学习方法预测过渡金属磷光体激发态性质
Low-cost machine learning approach to the prediction of transition metal phosphor excited state properties
摘要：
光活性铱配合物由于其应用范围从照明到照明而受到广泛关注。然而，这些复合物的激发态性质预测从准确性和计算成本的角度都挑战了从头算方法，例如依赖于时间的密度泛函理论（TDDFT），使高通量虚拟筛选（HTVS）复杂化。相反，我们利用低成本的机器学习（ML）模型来预测光敏铱络合物的激发态特性。我们使用1，380个铱配合物的实验数据来训练和评估ML模型，并从低成本的密度泛函理论紧束缚计算中确定性能最佳和最可转移的模型是那些在电子结构特征上训练的模型。使用这些模型，我们预测的三个激发态的性能考虑，磷光的平均发射能量，激发态寿命，和发射光谱积分，与TDDFT竞争或取代的准确性。我们进行功能的重要性分析，以确定铱复杂的属性管理激发态的属性，我们验证这些趋势与明确的例子。为了演示我们的ML模型如何用于HTVS和加速化学发现，我们策划了一组新的假设铱络合物，并确定了有前途的配体用于设计新的磷光体。
英文摘要：
Photoactive iridium complexes are of broad interest due to their applications ranging from lighting to photocatalysis. However, the excited state property prediction of these complexes challenges ab initio methods such as time-dependent density functional theory (TDDFT) both from an accuracy and a computational cost perspective, complicating high throughput virtual screening (HTVS). We instead leverage low-cost machine learning (ML) models to predict the excited state properties of photoactive iridium complexes. We use experimental data of 1,380 iridium complexes to train and evaluate the ML models and identify the best-performing and most transferable models to be those trained on electronic structure features from low-cost density functional theory tight binding calculations. Using these models, we predict the three excited state properties considered, mean emission energy of phosphorescence, excited state lifetime, and emission spectral integral, with accuracy competitive with or superseding TDDFT. We conduct feature importance analysis to identify which iridium complex attributes govern excited state properties and we validate these trends with explicit examples. As a demonstration of how our ML models can be used for HTVS and the acceleration of chemical discovery, we curate a set of novel hypothetical iridium complexes and identify promising ligands for the design of new phosphors.
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