论文标题
机器学习与债券信息以进行本地结构优化的表面科学优化
Machine Learning with bond information for local structure optimizations in surface science
论文作者
论文摘要
吸附系统的局部优化固有地涉及不同的尺度：在底物，分子内以及分子和底物之间。在这项工作中，我们展示了这些系统中债券不同特征的明确建模如何改善机器学习方法的优化性能。我们在高斯流程回归框架中引入了各向异性内核，该核心指导搜索本地最小值，我们在不同类型的原子系统上显示了其整体良好性能。与吸附系统上最快的标准优化方法相比，该方法显示的速度最高为两倍。此外，我们表明，有限的记忆方法不仅在整体计算资源方面都是有益的，而且可以进一步减少能量和力量计算。
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.
