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首页> 外文期刊>The journal of physical chemistry, C. Nanomaterials and interfaces >Optimization of Work Function via Bayesian Machine Learning Combined with First-Principles Calculation
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Optimization of Work Function via Bayesian Machine Learning Combined with First-Principles Calculation

机译：通过贝叶斯机器学习优化工作函数与第一原则计算相结合

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摘要

Work function is one of the most fundamental and important physical quantities in surface science. Materials with either lower work function or higher work function would find various applications, such as electronic devices and high-performance catalysts. However, it would be challenging to find a material with the optimal work function exploiting the all-search or random approach, whether it is based on an experimental or theoretical method. In this paper, we use the Bayesian optimization (BO) approach, which is one of the most powerful machine-learning tools for optimization, in order to effectively explore a candidate material with a higher or lower work function value out of hundreds of thousands of materials registered in a material database. We introduce a quick measure of the work function based on the depth of the Fermi level calculated from the first-principles computation for the crystalline bulk structure of a material. We call this the approximate work function, treating it as the objective function of our BO scheme. Since we do not need any time-consuming surface calculation with the slab model for the evaluation of the approximate work function, a quick search of a material with the highest or the lowest work function is achieved. As input variables for our BO implementation, we employ some bulk-specific properties of materials, which can be fetched from the database. The demonstration of our BO-based exploration of the database shows that materials with both low and high limits of the approximate work function can be discovered more efficiently in BO than a random exploration. The top 10 lowest work function materials thus found are in line with our chemical intuition in that all of them include either alkali or alkaline earth metal. On the other hand, we found the top 10 highest work function materials with amazement because they also include either alkali or alkaline earth metal and a lanthanide element.
机译：工作功能是表面科学中最基本和最重要的物质之一。具有较低工作功能或更高的工作功能的材料将找到各种应用，例如电子设备和高性能催化剂。然而，找到一种利用所有搜索或随机方法的最佳工作函数的材料将具有挑战性，无论是基于实验还是理论方法。在本文中，我们使用贝叶斯优化（BO）方法，这是最强大的优化机器学习工具之一，以有效地探索具有较高或更低的工作函数值的候选材料，其中数十万材料在材料数据库中注册。我们基于根据材料的晶体堆积结构的第一原理计算计算的费米水平的深度来介绍功效的快速测量。我们称之为近似的工作功能，将其视为我们博计划的目标函数。由于我们不需要任何耗时的表面计算与平板模型进行评估，以便对近似功函数进行评估，因此实现了最高或最低工作功能的材料的快速搜索。作为我们BO实现的输入变量，我们使用一些材料的批量特定属性，可以从数据库中获取。我们基于BO的数据库探索的示范表明，在博中可以在博中更有效地发现具有近似功函数的低和高限制的材料而不是随机探索。如此发现的前10种最低工作功能材料符合我们的化学直觉，因为它们中的所有化学直觉包括碱金属或碱土金属。另一方面，我们发现了前10名最高的工作功能材料，因为它们还包括碱金属或碱土金属和镧系元素。

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