基于键信息的表面科学局部结构优化机器学习
Machine Learning with bond information for local structure optimizations in surface science
摘要：
吸附系统的局部优化固有地涉及不同的尺度：在基质内，在分子内，以及在分子和基质之间。在这项工作中，我们展示了这些系统中债券的不同特征的显式建模如何提高机器学习方法的优化性能。我们在高斯过程回归框架中引入了一个各向异性的内核，该内核指导了对局部最小值的搜索，并且我们在不同类型的原子系统中显示了其整体良好的性能。该方法显示了最快的标准优化吸附系统的方法相比，一个因素的两个加速。此外，我们表明，一个有限的内存方法不仅有利于整体计算资源，但可以导致进一步减少能源和力的计算。
英文摘要：
Local optimization of adsorption systems inherently involves different scales: within the substrate, within the molecule, and between molecule and substrate. In this work, we show how the explicit modeling of the different character of the bonds in these systems improves the performance of machine learning methods for optimization. We introduce an anisotropic kernel in the Gaussian process regression framework that guides the search for the local minimum, and we show its overall good performance across different types of atomic systems. The method shows a speed-up of up to a factor two compared with the fastest standard optimization methods on adsorption systems. Additionally, we show that a limited memory approach is not only beneficial in terms of overall computational resources, but can result in a further reduction of energy and force calculations.
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