论文标题
机器学习和激发的分子动力学
Machine learning and excited-state molecular dynamics
论文作者
论文摘要
在量子化学研究领域，机器学习的使用率越来越高。尽管大多数方法针对的是在其电子接地状态下研究化学系统的研究，但将光线纳入过程会导致电子激发态，并引起了一些新的挑战。在这里，我们根据机器学习调查了激发状态动态的最新进展。在此过程中，我们重点介绍了用于光引起的分子过程的机器学习方法的成功，陷阱，挑战和未来途径。
Machine learning is employed at an increasing rate in the research field of quantum chemistry. While the majority of approaches target the investigation of chemical systems in their electronic ground state, the inclusion of light into the processes leads to electronically excited states and gives rise to several new challenges. Here, we survey recent advances for excited-state dynamics based on machine learning. In doing so, we highlight successes, pitfalls, challenges and future avenues for machine learning approaches for light-induced molecular processes.
