Charting the Atomic C Interaction with Transition Metal Surfaces

Accessing the usefulness of atomic adsorption configurations in predicting the adsorption properties of molecules with machine learning

We present a systematic study into the effect of adding atomic adsorption configurations into the training and validation dataset for a neural network's predictions of the adsorption energies of small molecules on single metal and bimetallic, single crystal surfaces. Specifically, we examine the efficacy of models trained with and without H and X atomic adsorption configurations, where X is C, N, or O, to predict XHn adsorption energies. In addition, we compare our machine learning models to traditional simple scaling relationships. We find that models trained with the atomic adsorption configurations outperform models trained with only molecular adsorption configurations, with as much as a 0.37 eV decrease in the MAE. We find that models trained with the atomic adsorption configurations slightly outperform traditional scaling relationships. In general, these results suggest it may be possible to vastly reduce the number of adsorption configurations one needs for training and validation datasets by supplementing said data with the adsorption configurations of composite atoms or smaller molecular fragments.

Machine Learning-Driven Discovery of Key Descriptors for CO2 Activation over Two-Dimensional Transition Metal Carbides and Nitrides

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 CO₂ 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 CO₂ 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 d-band center (ε_d), surface metal electronegativity (χ_M), and valence electron number of metal atoms (M_V) as key descriptors for CO₂ activation. These findings furnish a fundamental basis for designing novel MXene-based catalysts through the prediction of potential indicators for CO₂ activation and their posterior usage.

Recent Advances in Carbon Dioxide Adsorption, Activation and Hydrogenation to Methanol using Transition Metal Carbides

The inevitable emission of carbon dioxide (CO₂ ) due to the burning of a substantial amount of fossil fuels has led to serious energy and environmental challenges. Metal-based catalytic CO₂ transformations into commodity chemicals are a favorable approach in the CO₂ mitigation strategy. Among these transformations, selective hydrogenation of CO₂ to methanol is the most promising process that not only fulfils the energy demands but also re-balances the carbon cycle. The investigation of CO₂ adsorption on the surface of heterogeneous catalyst is highly important because the formation of various intermediates which determines the selectivity of product. Transition metal carbides (TMCs) have received considerable attention in recent years because of their noble metal-like reactivity, ceramic-like properties, high chemical and thermal stability. These features make them excellent catalytic materials for a variety of transformations such as CO₂ adsorption and its conversion into value-added chemicals. Herein, the catalytic properties of TMCs are summarize along with synthetic methods, CO₂ binding modes, mechanistic studies, effects of dopant on CO₂ adsorption, and carbon/metal ratio in the CO₂ hydrogenation reaction to methanol using computational as well as experimental studies. Additionally, this Review provides an outline of the challenges and opportunities for the development of potential TMCs in CO₂ hydrogenation reactions.