Microkinetic analysis based on density functional theory (DFT) was combined with a generative adversarial network (GAN) to enable the artificial proposal of heterogeneous catalysts based on the DFT-calculated dataset. The approach was applied to the NH₃ formation reaction on Rh−Ru alloy surfaces as an example. The NH₃ formation turnover frequency (TOF) was calculated by DFT-based microkinetics. Six elementary reactions, namely, N₂ dissociation, H₂ dissociation, NH_x (x = 1–3) formation, and NH₃ desorption, were explicitly considered, and their reaction energies were evaluated by DFT calculations. Based on the TOF values and atomic compositions, new alloy surfaces were generated using the GAN. This approach successfully generated the surfaces that were not included in the initial dataset but exhibited higher TOF values. The N₂ dissociation reaction was more exothermic for the generated surfaces, leading to higher TOF. The present study demonstrates that the automatic improvement of catalyst materials is possible using DFT calculations and GAN sample generation.

基于密度泛函理论 (DFT) 的微动力学分析与生成对抗网络 (GAN) 相结合，以实现基于 DFT 计算数据集的非均相催化剂的人工提议。该方法以在 Rh-Ru 合金表面上的 NH ₃形成反应为例。NH ₃形成周转频率(TOF) 通过基于DFT 的微动力学计算。六个基本反应，即N ₂解离、H ₂解离、NH _x ( x  = 1-3) 形成和NH ₃解吸，被明确考虑，并通过 DFT 计算评估它们的反应能。基于 TOF 值和原子组成，使用 GAN 生成了新的合金表面。这种方法成功地生成了未包含在初始数据集中但表现出更高 TOF 值的表面。N ₂解离反应对生成的表面更放热，导致更高的 TOF。本研究表明，使用 DFT 计算和 GAN 样本生成可以自动改进催化剂材料。