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.

In computer simulations of solvation effects on chemical reactions, continuum modeling techniques regain popularity as a way to efficiently circumvent an otherwise costly sampling of solvent degrees of freedom. As effective techniques, such implicit solvation models always depend on a number of parameters that need to be determined earlier. In the past, the focus lay mostly on an accurate parametrization of water models. Yet, non-aqueous solvents have recently attracted increasing attention, in particular, for the design of battery materials. To this end, we present a systematic parametrization protocol for the Self-Consistent Continuum Solvation (SCCS) model resulting in optimized parameters for 67 non-aqueous solvents. Our parametrization is based on a collection of ≈6000 experimentally measured partition coefficients, which we collected in the Solv@TUM database presented here. The accuracy of our optimized SCCS model is comparable to the well-known universal continuum solvation model (SMx) family of methods, while relying on only a single fit parameter and thereby largely reducing statistical noise. Furthermore, slightly modifying the non-electrostatic terms of the model, we present the SCCS-P solvation model as a more accurate alternative, in particular, for aromatic solutes. Finally, we show that SCCS parameters can, to a good degree of accuracy, also be predicted for solvents outside the database using merely the dielectric bulk permittivity of the solvent of choice.

The visualization of data is indispensable in scientific research, from the early stages when human insight forms, to the final step of communicating results. In computational physics, 1 chemistry and materials science, it can be as simple as making a scatter plot, or as straightforward as looking through the snapshots of atomic positions manually. However, as a result of the "big data" revolution these conventional approaches are often inadequate. The widespread adoption of high-throughput computation for materials discovery and the associated community-wide repositories have given rise to data sets that contain an enormous number of compounds and atomic configurations. A typical data set contains thousands to millions of atomic structures, along with a diverse range of properties such as formation energies, band gaps, or bio-activities.It would thus be desirable to have a data-driven and automated framework for visualizing and analyzing such structural datasets. The key idea is to construct a low-dimensional representation of the data, which facilitates navigation, reveals underlying patterns, and helps to identify data points with unusual attributes. Such data-intensive maps, often employing machine learning methods, are appearing more and more frequently in the literature. However, to the wider community, it is not always transparent how these maps are made and how they should be interpreted. Furthermore, while these maps undoubtedly serve a decorative purpose in academic publications, it is not always apparent what extra information can be garnered from reading or making them. This Account attempts to answer such questions. We start with a concise summary of the theory of representing chemical environments, followed by the introduction of a simple yet practical conceptual approach for generating structure maps in a generic and automated manner. Such analysis and mapping is made nearly effortless by employing the newly developed software tool, ASAP. To showcase the applicability to a wide variety of systems in chemistry and materials science, we provide several illustrative examples, including crystalline and amorphous materials, interfaces, and organic molecules. In these examples, the maps not only help to sift through large datasets, but also reveal hidden patterns that could be easily missed using conventional analyses.The explosion in the amount of computed information in chemistry and materials science 2 has made visualization into a science in itself. Not only have we benefited from exploiting these visualization methods in previous works, we also believe that the automated mapping of datasets will in turn stimulate further creativity and exploration, as well as ultimately feed back into future advances in the respective fields.

Data science and machine learning in materials science require large datasets of technologically relevant molecules or materials. Currently, publicly available molecular datasets with realistic molecular geometries and spectral properties are rare. We here supply a diverse benchmark spectroscopy dataset of 61,489 molecules extracted from organic crystals in the Cambridge Structural Database (CSD), denoted OE62. Molecular equilibrium geometries are reported at the Perdew-Burke-Ernzerhof (PBE) level of density functional theory (DFT) including van der Waals corrections for all 62k molecules. For these geometries, OE62 supplies total energies and orbital eigenvalues at the PBE and the PBE hybrid (PBE0) functional level of DFT for all 62k molecules in vacuum as well as at the PBE0 level for a subset of 30,876 molecules in (implicit) water. For 5,239 molecules in vacuum, the dataset provides quasiparticle energies computed with many-body perturbation theory in the G0W0 approximation with a PBE0 starting point (denoted GW5000 in analogy to the GW100 benchmark set (M. van Setten et al. J. Chem. Theory Comput. 12, 5076 (2016))).

Improving charge carrier mobilities in organic semiconductors is a challenging task that has hitherto primarily been tackled by empirical structural tuning of promising core compounds. Knowledge-based methods can greatly accelerate such local exploration, while a systematic analysis of large chemical databases can point towards promising design strategies. Here, we demonstrate such data mining by clustering an inhouse database of > 64.000 organic molecular crystals for which two charge-transport descriptors, the electronic coupling and the reorganization energy, have been calculated from first principles. The clustering is performed according to the Bemis-Murcko scaffolds of the constituting molecules and according to the sidegroups with which these molecular backbones are functionalized. In both cases, we obtain statistically significant structure-property relationships with certain scaffolds (sidegroups) consistently leading to favorable charge-transport properties. Functionalizing promising scaffolds

The versatility of organic molecules generates a rich design space for organic semiconductors (OSCs) considered for electronics applications. Offering unparalleled promise for materials discovery, the vastness of this design space also dictates efficient search strategies. Here, we present an active machine learning (AML) approach that explores an unlimited search space through consecutive application of molecular morphing operations. Evaluating the suitability of OSC candidates on the basis of charge injection and mobility descriptors, the approach successively queries predictive-quality first-principles calculations to build a refining surrogate model. The AML approach is optimized in a truncated test space, providing deep methodological insight by visualizing it as a chemical space network. Significantly outperforming a conventional computational funnel, the optimized AML approach rapidly identifies well-known and hitherto unknown molecular OSC candidates with superior charge conduction properties. Most importantly, it constantly finds further candidates with highest efficiency while continuing its exploration of the endless design space.