固体表面化学：吸附能的高通量自动预测
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确定分子吸附能，对于搜索用于电子器件和催化剂的最佳材料来说至关重要。密度泛函理论可以预测吸附能，但通常需要人们凭直觉来调整计算构型。由于固体表面和被吸附物的组合众多，选定最佳构型组合耗时费力。来自美国加州劳伦斯伯克利国家实验室的Joseph Montoya和加州大学伯克利分校的Kristin Persson采用Materials Project开源基础设施里面的计算工具，提出了使用密度泛函理论来计算固体表面吸附能的高通量计算流程，将任意表面-被吸附底物之间不同构型的对称构建做了程序自动化，并把计算过程的用户人为干预降到最低，做到准确计算分子在固体表面吸附能。该研究所提出的计算方法可加快理论指导的新材料发现，促进催化和表面科学的发展。该文近期发表于npj Computational Materials 3, Article number: 14 (2017)； doi:10.1038/s41524-017-0017-z，英文标题与摘要如下。点击阅读原文可以自由下载论文PDF。
A high-throughput framework for determining adsorption energies on solid surfaces
Joseph H. Montoya & Kristin A. Persson
In this work, we present a high-throughput workflow for calculation of adsorption energies on solid surfaces using density functional theory. Using open-source computational tools from the Materials Project infrastructure, we automate the procedure of constructing symmetrically distinct adsorbate configurations for arbitrary slabs. These algorithms are further used to construct and run workflows in a standard, automated way such that user intervention in the simulation procedure is minimal. To validate our approach, we compare results from our workflow to previous experimental and theoretical benchmarks from the CE27 database of chemisorption energies on solid surfaces. These benchmarks also illustrate how the task of performing and managing over 200 individual density functional theory calculations may be reduced to a single submission procedure and subsequent analysis. By enabling more efficient high-throughput computations of adsorption energies, these tools will accelerate theory-guided discovery of advanced materials for applications in catalysis and surface science.
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