Transition metal carbides are posed as promising materials for carbon dioxide (CO2) capture and storage at room temperature and low pressures, as shown by density functional simulations on proper models, and estimates of adsorption/desorption rates. Aside, the activated nature of the adsorbed CO2 opens the path for its conversion into other valuable chemicals.

Present experiments show that synthesized polycrystalline hexagonal α-Mo 2 C is a highly efficient and selective catalyst for CO 2 uptake and conversion to CO through the reverse water gas shift reaction. The CO 2 conversion is ~16% at 673 K, with selectivity towards CO > 99%. CO 2 and CO adsorption is monitored by DRIFTS, TPD, and show that polycrystalline α-Mo 2 C is an economically viable, highly efficient, and selective catalyst for CO generation using CO 2 as a feedstock.

Instant machine learning predictions of molecular properties are desirable for materials design, but the predictive power of the methodology is mainly tested on well-known benchmark datasets. Here, we investigate the performance of machine learning with kernel ridge regression (KRR) for the prediction of molecular orbital energies on three large datasets: the standard QM9 small organic molecules set, amino acid and dipeptide conformers, and organic crystal-forming molecules extracted from the Cambridge Structural Database. We focus on prediction of highest occupied molecular orbital (HOMO) energies, computed at density-functional level of theory. Two different representations that encode molecular structure are compared: the Coulomb matrix (CM) and the many-body tensor representation (MBTR). We find that KRR performance depends significantly on the chemistry of the underlying dataset and that the MBTR is superior to the CM, predicting HOMO energies with a mean absolute error as low as 0.09 eV. To demonstrate the power of our machine learning method, we apply our model to structures of 10k previously unseen molecules. We gain instant energy predictions that allow us to identify interesting molecules for future applications.