Department of Chemical Engineering, Indian Institute of Technology Kanpur, Kanpur 208016, India.
Departament de Ciència de Materials i Química Física, Institut de Química Teòrica i Computacional (IQTCUB), Universitat de Barcelona, c/ Martí i Franquès 1-11, Barcelona 08028, Spain.
ACS Appl Mater Interfaces. 2023 Jun 28;15(25):30117-30126. doi: 10.1021/acsami.3c02821. Epub 2023 Jun 19.
Fusing high-throughput quantum mechanical screening techniques with modern artificial intelligence strategies is among the most fundamental ─yet revolutionary─ science activities, capable of opening new horizons in catalyst discovery. Here, we apply this strategy to the process of finding appropriate key descriptors for CO activation over two-dimensional transition metal (TM) carbides/nitrides (MXenes). Various machine learning (ML) models are developed to screen over 114 pure and defective MXenes, where the random forest regressor (RFR) ML scheme exhibits the best predictive performance for the CO adsorption energy, with a mean absolute error ± standard deviation of 0.16 ± 0.01 and 0.42 ± 0.06 eV for training and test data sets, respectively. Feature importance analysis revealed -band center (ε), surface metal electronegativity (χ), and valence electron number of metal atoms () as key descriptors for CO activation. These findings furnish a fundamental basis for designing novel MXene-based catalysts through the prediction of potential indicators for CO activation and their posterior usage.
将高通量量子力学筛选技术与现代人工智能策略相结合，是最基础的——同时也是极具变革性的——科学活动之一，能够为催化剂的发现开辟新的视野。在这里，我们将这一策略应用于寻找二维过渡金属（TM）碳化物/氮化物（MXenes）上 CO 活化的合适关键描述符的过程中。我们开发了各种机器学习（ML）模型来筛选超过 114 种纯的和有缺陷的 MXenes，其中随机森林回归器（RFR）ML 方案对 CO 吸附能表现出最佳的预测性能，训练集和测试集的平均绝对误差 ± 标准差分别为 0.16 ± 0.01 和 0.42 ± 0.06 eV。特征重要性分析揭示了能带中心（ε）、表面金属电负性（χ）和金属原子的价电子数（）是 CO 活化的关键描述符。这些发现为通过预测 CO 活化的潜在指标及其后续使用来设计新型 MXene 基催化剂提供了一个基本的基础。
