Simulating Segregation in a Ternary Cu–Pd–Au Alloy with Density Functional Theory, Machine Learning, and Monte Carlo Simulations
Simulating Segregation in a Ternary Cu–Pd–Au Alloy with Density Functional Theory, Machine Learning, and Monte Carlo Simulations
复制标题
利用密度泛函理论、机器学习和蒙特卡罗模拟模拟三元 Cu-Pd-Au 合金中的偏析
DOI：
10.1021/acs.jpcc.1c09647
复制
发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Kitchin, John R.
中科院分区：
文献类型：
--
作者：
Yang, Yilin;Guo, Zhitao;Gellman, Andrew J.;Kitchin, John R.
模拟多元合金的偏析分布对于研究合金催化剂的催化性能具有重要意义。密度泛函理论（DFT）是太昂贵了，直接使用来评估在模拟过程中的平板配置的势能。在这项工作中，我们建立了一个基于5278 DFT计算的神经网络（NN）作为代理模型来评估三元Cu-Pd-Au合金fcc（111）板的势能。训练后的神经网络能够以高精度预测整个三元空间的Cu-Pd-Au势能。将神经网络与Monte Carlo模拟相结合，得到了600 K时Cu-Pd-Au在体相组分空间的分凝分布。模拟结果与PdAu和CuAu的实验数据定性一致，但它们是不正确的沿着PdCu线。进一步的DFT计算表明，在实际条件下，理想面心立方（111）晶面不能捕获CuPd在欠配位表面上的偏聚行为.
Simulation of the segregation profile of multicomponent alloys is important to investigate the catalytic properties of alloy catalysts. Density functional theory (DFT) is too expensive to use directly to evaluate the potential energies of the slab configurations during the simulations. In this work, we build a neural network (NN) based on 5278 DFT calculations as a surrogate model to evaluate the potential energies of the fcc(111) slabs for a ternary Cu–Pd–Au alloy. The trained NN is capable of predicting the Cu–Pd–Au potential energies across the whole ternary space with high accuracy. Combining the NN with Monte Carlo simulation, we obtained the segregation profile of Cu–Pd–Au at 600 K across the bulk composition space. The simulation results are qualitatively consistent with the experimental data for PdAu and CuAu, but they are incorrect along the PdCu line. Further DFT calculations show that the perfect fcc(111) slab is not capable of capturing the CuPd segregation behavior on undercoordinated surfaces under the realistic conditions.