纳米尺度金属团簇中原子binding energies的人工智能实时预测和物理解释
Artificial intelligence real-time prediction and physical interpretation of atomic binding energies in nano-scale metal clusters
摘要：
单原子位置通常决定材料的功能和性能，如催化剂，半导体或酶。因此，计算和理解这些网站的属性是合理的材料设计过程的一个重要组成部分。由于原子水平上复杂的电子效应，原子位置特性通常是从计算上昂贵的第一原理计算得出的，因为需要这种理论水平来实现相关的准确性。在这项研究中，我们提出了一种广泛适用的机器学习（ML）方法来实时计算原子位置属性，具有高精度。该方法适用于复杂的非晶原子结构，因此开辟了高通量筛选纳米材料，无定形系统和材料界面的可能性。我们的方法包括一个强大的特征化方案，将原子结构转换为可以由常见的机器学习模型使用的功能。执行遗传算法（GA）为基础的功能选择，我们展示了如何建立一个直观的物理解释的ML模型所隐含的结构-性能关系。用这种方法，我们计算的金属纳米粒子的原子位置的稳定性，从3-55个原子的平均绝对误差在0.11-0.14 eV的范围内实时。我们还建立了网站的化学身份作为最重要的因素，在确定原子网站的稳定性，其次是结构特征，如键距和角度。特征化和GA特征选择功能都发布在开源Python模块中。通过这种方法，我们可以通过数据驱动的材料筛选来高效合理地设计高度专业化的现实世界纳米催化剂。
英文摘要：
Single atomic sites often determine the functionality and performance of materials, such as catalysts, semi-conductors or enzymes. Computing and understanding the properties of such sites is therefore a crucial component of the rational materials design process. Because of complex electronic effects at the atomic level, atomic site properties are conventionally derived from computationally expensive first-principle calculations, as this level of theory is required to achieve relevant accuracy. In this study, we present a widely applicable machine learning (ML) approach to compute atomic site properties with high accuracy in real time. The approach works well for complex non-crystalline atomic structures and therefore opens up the possibility for high-throughput screenings of nano-materials, amorphous systems and materials interfaces. Our approach includes a robust featurization scheme to transform atomic structures into features which can be used by common machine learning models. Performing a genetic algorithm (GA) based feature selection, we show how to establish an intuitive physical interpretation of the structure-property relations implied by the ML models. With this approach, we compute atomic site stabilities of metal nanoparticles ranging from 3-55 atoms with mean absolute errors in the range of 0.11-0.14 eV in real time. We also establish the chemical identity of the site as most important factor in determining atomic site stabilities, followed by structural features like bond distances and angles. Both, the featurization and GA feature selection functionality are published in open-source python modules. With this method, we enable the efficient rational design of highly specialized real-world nano-catalysts through data-driven materials screening.
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