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arXiv 1701.01897astro-ph.GAphysics.chem-ph
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用机器学习方法获得O-H碰撞中精细结构激发的速率常数

Rate Constants for Fine-Structure Excitations in O-H Collisions with Error Bars Obtained by Machine Learning

Daniel Vieira, Roman Krems

摘要:

我们提出了一种方法,使用耦合通道散射计算与基于高斯过程回归的机器学习技术相结合,以确定非弹性原子碰撞中非绝热跃迁的速率常数对底层绝热相互作用势变化的敏感性。利用这种方法,我们改进了以前O(3Pj)与H原子碰撞中精细结构跃迁速率常数的计算。我们计算的误差条的速率常数对应于20%的从头算电位的变化,并表明,这种方法可以用来确定哪些个别的绝热势是或多或少重要的不同的精细结构变化碰撞的结果。

英文摘要:

We present an approach using a combination of coupled channel scattering calculations with a machine- learning technique based on Gaussian Process regression to determine the sensitivity of the rate constants for non-adiabatic transitions in inelastic atomic collisions to variations of the underlying adiabatic interaction potentials. Using this approach, we improve the previous computations of the rate constants for the fine-structure transitions in collisions of O(3Pj) with atomic H. We compute the error bars of the rate constants corresponding to 20 % variations of the ab initio potentials and show that this method can be used to determine which of the individual adiabatic potentials are more or less important for the outcome of different fine-structure changing collisions.

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