论文标题
使用Schnarc的紫外吸收光谱的深度学习：在化合物空间中转移性的第一步
Deep Learning for UV Absorption Spectra with SchNarc: First Steps Towards Transferability in Chemical Compound Space
论文作者
论文摘要
机器学习（ML）已证明可以在几乎任何可能的方向上推进量子化学的研究领域，并且最近还进入激发态以研究分子的多方面光化学。在本文中，我们实现了两个目标：i）我们展示了如何使用ML通过调整[Chem。 Sci。，2017，8，6924-6935]，最初是针对电子基态的永久性偶极矩向量提出的。 ii）我们研究了激发态ML模型在化学空间中的可传递性，即ML模型是否可以预测其从未受过训练的分子的特性，以及它是否可以同时学习两个分子的不同激发态。为此，我们采用并扩展了先前报道的SCHNARC方法用于激发态ML。我们从激发态能量和过渡偶极矩和静电电势中计算出由永久偶极矩向量的ML模型推断出的潜在电荷的紫外吸收光谱。我们在CH $ _2 $ nh $ _2^+$和C $ _2 $ h $ _4 $上训练ML型号，而对这些分子进行预测，此外还针对CHNH $ _2 $，CH $ _2 $ NH，以及C $ _2 $ _2 $ _2 $ H $ _5^+$。结果表明激发态可以转移性。
Machine learning (ML) has shown to advance the research field of quantum chemistry in almost any possible direction and has recently also entered the excited states to investigate the multifaceted photochemistry of molecules. In this paper, we pursue two goals: i) We show how ML can be used to model permanent dipole moments for excited states and transition dipole moments by adapting the charge model of [Chem. Sci., 2017, 8, 6924-6935], which was originally proposed for the permanent dipole moment vector of the electronic ground state. ii) We investigate the transferability of our excited-state ML models in chemical space, i.e., whether an ML model can predict properties of molecules that it has never been trained on and whether it can learn the different excited states of two molecules simultaneously. To this aim, we employ and extend our previously reported SchNarc approach for excited-state ML. We calculate UV absorption spectra from excited-state energies and transition dipole moments as well as electrostatic potentials from latent charges inferred by the ML model of the permanent dipole moment vectors. We train our ML models on CH$_2$NH$_2^+$ and C$_2$H$_4$, while predictions are carried out for these molecules and additionally for CHNH$_2$, CH$_2$NH, and C$_2$H$_5^+$. The results indicate that transferability is possible for the excited states.
