CH $_2$ NH $_2^+$：从单状态到多状态表示和多属性机器学习模型
Neural networks and kernel ridge regression for excited states dynamics of CH$_2$NH$_2^+$: From single-state to multi-state representations and multi-property machine learning models
摘要：
激发态动力学模拟是研究分子和材料的光致反应并为实验提供补充信息的有力工具。由于这些模拟技术的适用性受到底层电子结构计算成本的限制，我们开发并评估了用于此任务的不同机器学习模型。使用亚甲基亚铵阳离子（CH $_2$ NH $_2^+$）作为模型系统，在激发电子态的{ \emph ab initio}计算上训练机器学习模型。对于激发态性质的预测，需要多个输出，这对于神经网络来说是直接的，但对于核岭回归来说探索较少。我们克服了这一挑战的内核岭回归的情况下，能源预测的电子状态明确编码的输入，除了分子表示。我们也采用这种策略用于我们的神经网络进行比较。这样的状态编码不仅能够实现具有多个输出的核岭回归，而且还可以为特定于状态的属性提供更准确的机器学习模型。激励状态机器学习模型的一个重要目标是它们在动力学仿真中的使用，这不仅需要特定于状态的信息，还需要耦合，即，涉及状态对的性质。因此，我们调查的性能不同的模型，这样的耦合元件。此外，我们还探索了在单个神经网络中组合所有属性如何影响准确性。作为对我们的机器学习模型的最终测试，我们根据预测的能量、力和耦合进行了激发态动力学模拟，从而展示了机器学习治疗电子激发态的范围和可能性。
英文摘要：
Excited-state dynamics simulations are a powerful tool to investigate photo-induced reactions of molecules and materials and provide complementary information to experiments. Since the applicability of these simulation techniques is limited by the costs of the underlying electronic structure calculations, we develop and assess different machine learning models for this task. The machine learning models are trained on {\emph ab initio} calculations for excited electronic states, using the methylenimmonium cation (CH$_2$NH$_2^+$) as a model system. For the prediction of excited-state properties, multiple outputs are desirable, which is straightforward with neural networks but less explored with kernel ridge regression. We overcome this challenge for kernel ridge regression in the case of energy predictions by encoding the electronic states explicitly in the inputs, in addition to the molecular representation. We adopt this strategy also for our neural networks for comparison. Such a state encoding enables not only kernel ridge regression with multiple outputs but leads also to more accurate machine learning models for state-specific properties. An important goal for excited-state machine learning models is their use in dynamics simulations, which needs not only state-specific information but also couplings, i.e., properties involving pairs of states. Accordingly, we investigate the performance of different models for such coupling elements. Furthermore, we explore how combining all properties in a single neural network affects the accuracy. As an ultimate test for our machine learning models, we carry out excited-state dynamics simulations based on the predicted energies, forces and couplings and, thus, show the scopes and possibilities of machine learning for the treatment of electronically excited states.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
