arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2108.09392cond-mat.mtrl-scicond-mat.mes-hall
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基于吸附质和基质性质的金属表面分子吸附可传递预测模型

A transferable prediction model of molecular adsorption on metals based on adsorbate and substrate properties

Paolo Restuccia, Ehsan A. Ahmad, Nicholas M. Harrison

摘要:

表面吸附是催化、环境、能源、医药等领域的基础过程之一。发展一个能在几分钟内有效预测结合能的吸附模型一直是表面和界面科学的一个长期目标。该解决方案一直难以捉摸,因为识别各种组合物、结构和环境的吸附能的内在决定因素是不平凡的。我们介绍了一个新的和灵活的模型来预测吸附能的金属基板。该模型是基于容易计算,基板和吸附质的固有特性。它是使用基于探针分子的第一性原理计算的机器学习来参数化的(例如,H $_2$ O,CO $_2$,O $_2$,N $_2$)吸附到一系列纯金属基底上。该模型预测的计算解离吸附能的金属表面的相关系数为0.93和平均绝对误差为0.77 eV的大数据库的分子吸附能提供的\textit{Catalysis-Hub.org},其中有一个范围为15 eV。由于该模型是基于预先计算的量,它提供了吸附能的近瞬时估计,它是足够准确的,以消除约90%的候选人在筛选研究的新的吸附质。该模型,因此,显着提高了目前的努力,以确定新的分子涂层在许多应用研究领域。

英文摘要:

Surface adsorption is one of the fundamental processes in numerous fields, including catalysis, environment, energy and medicine. The development of an adsorption model which provides an effective prediction of binding energy in minutes has been a long term goal in surface and interface science. The solution has been elusive as identifying the intrinsic determinants of the adsorption energy for various compositions, structures and environments is non-trivial. We introduce a new and flexible model for predicting adsorption energies to metal substrates. The model is based on easily computed, intrinsic properties of the substrate and adsorbate. It is parameterised using machine learning based on first-principles calculations of probe molecules (e.g., H$_2$O, CO$_2$, O$_2$, N$_2$) adsorbed to a range of pure metal substrates. The model predicts the computed dissociative adsorption energy to metal surfaces with a correlation coefficient of 0.93 and a mean absolute error of 0.77 eV for the large database of molecular adsorption energies provided by \textit{Catalysis-Hub.org} which have a range of 15 eV. As the model is based on pre-computed quantities it provides near-instantaneous estimates of adsorption energies and it is sufficiently accurate to eliminate around 90\% of candidates in screening study of new adsorbates. The model, therefore, significantly enhances current efforts to identify new molecular coatings in many applied research fields.

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。

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