论文标题
使用机器学习和物理启发的图表表示，预测复杂吸附物的结合基序
Predicting binding motifs of complex adsorbates using machine learning with a physics-inspired graph representation
论文作者
论文摘要
异质催化中的计算筛选越来越多地取决于机器学习模型，用于预测关键输入参数，因为使用第一原理方法直接计算这些参数。考虑复杂的材料空间，例如合金或与吸附物的复杂反应机制，可能表现出双偏或更高dentate吸附基序。在这里，我们提出了一种基于定制的WASSERSTEIN WEESSERTEIN WEISFEILER-LEHMAN图形内核和高斯过程回归的，在过渡金属（TMS）（TMS）（TMS）（TMS）（TMS）（TMS）及其合金中的结合基序的预测和相关的吸附焓的数据效率方法。该模型显示出良好的预测性能，不仅是对其训练的元素TM，而且还针对基于这些TMS的合金。此外，最少的新培训数据纳入可以预测室外TM。我们认为，该模型在主动学习方法中可能很有用，我们为此提供了一种集合的不确定性估计方法。
Computational screening in heterogeneous catalysis relies increasingly on machine learning models for predicting key input parameters due to the high cost of computing these directly using first-principles methods. This becomes especially relevant when considering complex materials spaces, e.g. alloys, or complex reaction mechanisms with adsorbates that may exhibit bi- or higher-dentate adsorption motifs. Here we present a data-efficient approach to the prediction of binding motifs and associated adsorption enthalpies of complex adsorbates at transition metals (TMs) and their alloys based on a customized Wasserstein Weisfeiler-Lehman graph kernel and Gaussian Process Regression. The model shows good predictive performance, not only for the elemental TMs on which it was trained, but also for an alloy based on these TMs. Furthermore, incorporation of minimal new training data allows for predicting an out-of-domain TM. We believe the model may be useful in active learning approaches, for which we present an ensemble uncertainty estimation approach.
