Benchmarking Computational Alchemy for Carbide, Nitride, and Oxide Catalysts
Benchmarking Computational Alchemy for Carbide, Nitride, and Oxide Catalysts
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DOI：
10.1002/adts.201800142
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发表时间：
2019-04-01
影响因子：
3.3
通讯作者：
Keith, John A.
中科院分区：
文献类型：
--
作者：
Griego, Charles D.;Saravanan, Karthikeyan;Keith, John A.
基于Kohn-Sham密度泛函理论（DFT）的假设催化剂的搜索计算量太大，无法通过不同的材料空间进行广泛的搜索。在这里，计算炼金术计划的准确性碳化物，氮化物和氧化物进行评估。通过一组参考DFT计算，计算炼金术近似了大量假设催化剂表面上的吸附物结合能（BE），计算成本可以忽略不计。与以前对金属合金的研究类似，计算炼金术预测了岩盐TiC（111），TiN（100）和TiO（100）材料上的吸附物BE，这些材料没有带隙，与DFT结果非常一致（平均无符号误差高达0.33 eV）。相比之下，发现半导体系统如金红石TiO 2（110）、金红石SnO 2（110）和岩盐ZnO（100）可能存在更大的挑战。这项工作确定了这些挑战与费米能级的态密度有关，并通过在TiO 2的表面层中添加Pt掺杂剂，表明计算炼金术可以在非过渡金属系统中变得更加可靠。这种补救措施提供了洞察力，促进计算炼金术广泛搜索催化剂活性位点，通过材料空间超越过渡金属合金。
Kohn-Sham density functional theory (DFT)-based searches for hypothetical catalysts are too computationally demanding for wide searches through diverse materials space. Here, the accuracy of computational alchemy schemes on carbides, nitrides, and oxides is assessed. With a single set of reference DFT calculations, computational alchemy approximates adsorbate binding energies (BEs) on a large number of hypothetical catalysts surfaces with negligible computational cost. Analogous to previous studies on metal alloys, computational alchemy predicts adsorbate BEs on rocksalt TiC(111), TiN(100), and TiO(100) materials, which have no bandgap, in close agreement with DFT results (with mean unsigned errors up to 0.33 eV). In contrast, it is found that semiconducting systems such as rutile TiO2(110), rutile SnO2(110), and rocksalt ZnO(100) can present more significant challenges. This work identifies these challenges being linked to the density of states at the Fermi level and by adding Pt dopants in the surface layer of TiO2, it is shown that computational alchemy can become more reliable with non-transition metal systems. This remedy provides insight that promotes computational alchemy for broad searches for catalyst active sites through materials space beyond transition metal alloys.