通过半经验/机器学习建模研究二维系统中的硫族杂质屏障：对4000种材料的调查
Chalcogen Impurity Barriers in 2D Systems via Semi-Empirical/Machine Learning Modeling: A Survey over 4000 Materials
AI总结：
本文提出结合半经验扩展Huckel方法与机器学习技术，研究硫族杂质在二维材料中的吸附能垒，通过分析4000种材料，确定影响吸附性质的关键属性。
摘要：
对具有低能量屏障的二维材料进行充分表征是推动基于催化、传感和表面功能化的应用发展的关键。然而，第一性原理方法，如DFT，通常在可行的大规模筛选中计算成本极高。鉴于这种情形，我们提出了一种数据驱动的方法，该方法结合了半经验扩展 Huckel 方法与机器学习技术，以估计三种相关硫族杂质（S、Se和Te）的吸附能量屏障。为此，我们考虑了C2DB中发现的4036种二维材料。该方案利用EHM计算沿三个平面迁移路径的能量剖面，从而推导出平均屏障。杂质与二维表面之间的平衡距离不是通过耗时的几何优化计算得出的，而是通过一个简单的有效现象学表达式进行估计。随后，从Matminer库中获得物理化学描述符用于定制特征。测试了四种不同的机器学习模型，其中XGBoost表现出最高性能。我们进一步使用SHAP验证结果，重点研究了显示最低屏障值的约1500种材料。正如预期的那样，我们确定平均价电子数、电负性和原子序数通常是验证机器学习模型最相关的属性。但我们也能够确定，对于不同的硫族原子，其他少数描述符同样显著影响吸附性质。我们的结果表明，当与可解释的机器学习协议结合时，EHM可以产生一个可扩展的框架，用于选择表现出所需捕获/释放动力学的二维结构，这在各种应用中都很相关。
英文摘要：
Adequate characterization of two-dimensional materials with low energy barriers for impurity adsorption is key for advancing applications based on catalysis, sensing, and surface functionalization. However, first-principles methods, such as DFT, are often computationally extremely expensive for feasible large-scale screenings. Given such a scenario, we address a data-driven approach which integrates the semi-empirical Extended Huckel Method with machine learning techniques to estimate adsorption energy barriers in the case of three relevant chalcogen impurities, S, Se and Te. With this aim, we consider the 4036 2D materials found in the C2DB. The scheme employs the EHM to compute energy profiles along three in-plane migration paths, from which average barriers can be derived. The equilibrium distance between the impurity and the 2D surface is not calculated from a tie-consuming geometry optimization. Instead, it is estimated from a simple effective phenomenological expression. Physicochemical descriptors are then obtained from the Matminer library for curated features. Four different ML models are tested, with the XGBoost leading to the highest performance. We further use SHAP to verify the resulting predictions, focusing on the $\sim1,500$ materials displaying the lowest barrier values. As it could be anticipated, we establish that the average valence electron count, electronegativity, and atomic number are typically the most relevant attributes to validate the ML model. But we also are able to determine, for the different chalcogen atoms, which other few descriptors likewise considerably influence the adsorption properties. Our results show that when combined with interpretable ML protocols, EHM can produce a scalable framework for choosing 2D structures that exhibit the desired capture/release dynamics pertinent in a variety of utilization.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
