Choksi Tej S, Streibel Verena, Abild-Pedersen Frank
SUNCAT Center for Interface Science and Catalysis, Department of Chemical Engineering, Stanford University, 443 Via Ortega, Stanford, California 94305, USA.
SUNCAT Center for Interface Science and Catalysis, SLAC National Accelerator Laboratory, 2575 Sand Hill Road, Menlo Park, California 94025, USA.
J Chem Phys. 2020 Mar 7;152(9):094702. doi: 10.1063/1.5141378.
Operando-computational frameworks that integrate descriptors for catalyst stability within catalyst screening paradigms enable predictions of rates and selectivity on chemically faithful representations of nanoparticles under reaction conditions. These catalyst stability descriptors can be efficiently predicted by density functional theory (DFT)-based models. The alloy stability model, for example, predicts the stability of metal atoms in nanoparticles with site-by-site resolution. Herein, we use physical insights to present accelerated approaches of parameterizing this recently introduced alloy-stability model. These accelerated approaches meld quadratic functions for the energy of metal atoms in terms of the coordination number with linear correlations between model parameters and the cohesive energies of bulk metals. By interpolating across both the coordination number and chemical space, these accelerated approaches shrink the training set size for 12 fcc p- and d-block metals from 204 to as few as 24 DFT calculated total energies without sacrificing the accuracy of our model. We validate the accelerated approaches by predicting adsorption energies of metal atoms on extended surfaces and 147 atom cuboctahedral nanoparticles with mean absolute errors of 0.10 eV and 0.24 eV, respectively. This efficiency boost will enable a rapid and exhaustive exploration of the vast material space of transition metal alloys for catalytic applications.
在催化剂筛选范式中整合催化剂稳定性描述符的操作计算框架，能够在反应条件下对纳米颗粒的化学忠实表示上的速率和选择性进行预测。这些催化剂稳定性描述符可以通过基于密度泛函理论（DFT）的模型有效地预测。例如，合金稳定性模型能够逐位点解析预测纳米颗粒中金属原子的稳定性。在此，我们运用物理见解，提出对这种最近引入的合金稳定性模型进行参数化的加速方法。这些加速方法将金属原子能量的二次函数与配位数相结合，并使模型参数与块状金属的内聚能之间具有线性相关性。通过在配位数和化学空间中进行插值，这些加速方法将12种面心立方p区和d区金属的训练集大小从204个密度泛函理论计算的总能缩小至仅24个，同时又不牺牲模型的准确性。我们通过预测金属原子在扩展表面和147个原子的立方八面体纳米颗粒上的吸附能来验证这些加速方法，平均绝对误差分别为0.10电子伏特和0.24电子伏特。这种效率提升将能够对用于催化应用的过渡金属合金的广阔材料空间进行快速且详尽的探索。
