Li Xinyu, Chiong Raymond, Page Alister J
School of Electrical Engineering and Computing, The University of Newcastle, Callaghan, New South Wales 2308, Australia.
Discipline of Chemistry, School of Environmental and Life Sciences, The University of Newcastle, Callaghan, New South Wales 2308, Australia.
J Phys Chem Lett. 2021 Jun 3;12(21):5156-5162. doi: 10.1021/acs.jpclett.1c01319. Epub 2021 May 25.
Machine learning has recently emerged as an efficient and powerful alternative to density functional theory for studying heterogeneous catalysis. Machine learning methods rely on a geometrical representation of the chemical environment around the catalytic adsorption site based on physical or chemical descriptors. Here, we show that replacing the atomic number in geometrical representations with elemental groups and periods (GP) yields significant improvements in predicted adsorption energies on bimetallic alloy surfaces. Notably, the GP-based Labeled Site Crystal Graph representation reported here achieves mean absolute error (MAE) ∼0.05 eV (near chemical accuracy) in predicting hydrogen adsorption and MAE ∼0.10 eV for other strong binding adsorbates such as carbon, nitrogen, oxygen, and sulfur. We also show GP-based representations to be robust in predicting adsorption on surface facets, elements, and alloys that are not included in the initial training set. This reliability makes GP-based representations an ideal basis for high-throughput approaches and materials discovery based on active learning techniques, which often involve limited training sets.
机器学习最近已成为一种高效且强大的替代方法，用于研究非均相催化，以取代密度泛函理论。机器学习方法基于物理或化学描述符，依赖于催化吸附位点周围化学环境的几何表示。在此，我们表明，用元素组和周期（GP）取代几何表示中的原子序数，可显著提高双金属合金表面预测吸附能的准确性。值得注意的是，本文报道的基于GP的标记位点晶体图表示法在预测氢吸附时实现了平均绝对误差（MAE）约为0.05 eV（接近化学精度），对于其他强结合吸附质（如碳、氮、氧和硫）的MAE约为0.10 eV。我们还表明，基于GP的表示法在预测未包含在初始训练集中的表面晶面、元素和合金上的吸附时具有稳健性。这种可靠性使得基于GP的表示法成为高通量方法和基于主动学习技术的材料发现的理想基础，而主动学习技术通常涉及有限的训练集。
