分子激发和光电发射光谱的物理启发深度学习
Physically inspired deep learning of molecular excitations and photoemission spectra
摘要：
现代功能材料由具有显著化学复杂性的大分子构建块组成，这限制了用精确的第一原理方法预测光谱性质。因此，如果没有有效的方法来预测整个化学空间的分子激发态特性，通过高通量筛选具有定制光电特性的材料的目标设计必然会失败。在这项工作中，我们提出了一个深度神经网络，预测带电准粒子激发大型和复杂的有机分子具有丰富的元素多样性和尺寸远远超出了准确的许多体微扰理论计算。该模型利用分子共振的基本物理作为潜在的哈密顿矩阵的本征值，因此能够同时准确地描述多个共振。该模型的性能证明了一系列的有机分子在化学组成空间和配置空间。我们进一步展示了模型的能力，通过预测光电子能谱在GW近似的水平，以前看不见的共轭分子。
英文摘要：
Modern functional materials consist of large molecular building blocks with significant chemical complexity which limits spectroscopic property prediction with accurate first-principles methods. Consequently, a targeted design of materials with tailored optoelectronic properties by high-throughput screening is bound to fail without efficient methods to predict molecular excited-state properties across chemical space. In this work, we present a deep neural network that predicts charged quasiparticle excitations for large and complex organic molecules with a rich elemental diversity and a size well out of reach of accurate many body perturbation theory calculations. The model exploits the fundamental underlying physics of molecular resonances as eigenvalues of a latent Hamiltonian matrix and is thus able to accurately describe multiple resonances simultaneously. The performance of this model is demonstrated for a range of organic molecules across chemical composition space and configuration space. We further showcase the model capabilities by predicting photoemission spectra at the level of the GW approximation for previously unseen conjugated molecules.
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