First-principles embedded-cluster calculations of the neutral and charged oxygen vacancy at the rutile TiO₂(110) surface

We perform full-potential screened-hybrid density-functional theory calculations to compare the thermodynamic stability of neutral and charged states of the surface oxygen vacancy at the rutile TiO2(110) surface. Solid-state (QM/MM) embedded-cluster calculations are employed to account for the strong TiO2 polarization response to the charged defect states. Similarly to the situation for the bulk O vacancy, the +2 charge state VO2+ is found to be energetically by far the most stable. Only for Fermi-level positions very close to the conduction band, small polarons may at best be trapped by the charged vacancy. The large decrease in the VO2+ formation energy with decreasing Fermi-level position indicates strongly enhanced surface O vacancy concentrations for p-doped samples

First-Principles Free-Energy Barriers for Photoelectrochemical Surface Reactions: Proton Abstraction at TiO₂(110)

We explicitly calculate the free-energy barrier for the initial proton abstraction in the water splitting reaction at rutile TiO2(110) through ab initio molecular dynamics. Combining solid-state embedding, an energy based reaction coordinate and state-of-the-art free-energy reconstruction techniques renders the calculation tractable at the hybrid density-functional theory level. The obtained free-energy barrier of approximately 0.2 eV, depending slightly on the orientation of the first acceptor water molecule, suggests a hindered reaction on the pristine rutile surface

Transferable ionic parameters for first-principles Poisson-Boltzmann solvation calculations: Neutral solutes in aqueous monovalent salt solutions

Implicit solvation calculations based on a Stern-layer corrected size-modified Poisson-Boltzmann (SMPB) model are an effective approach to capture electrolytic effects in first-principles electronic structure calculations. For a given salt solution, they require a range of ion-specific parameters, which describe the size of the dissolved ions as well as thickness and shape of the Stern layer. Out of this defined parameter space, we show that the Stern layer thickness expressed in terms of the solute’s electron density and the resulting ionic cavity volume completely determine ion effects on the stability of neutral solutes. Using the efficient SMPB functionality of the full-potential density-functional theory package FHI-aims, we derive optimized such Stern layer parameters for neutral solutes in various aqueous monovalent electrolytes. The parametrization protocol relies on fitting to reference Setschenow coefficients that describe solvation free energy changes with ionic strength at low to medium concentrations. The availability of such data for NaCl solutions yields a highly predictive SMPB model that allows to recover the measured Setschenow coefficients with an accuracy that is comparable to prevalent quantitative regression models. Correspondingly derived SMPB parameters for other salts suffer from a much scarcer experimental data base but lead to Stern layer properties that follow a physically reasonable trend with ionic hydration numbers

Perspective: On the active site model in computational catalyst screening

First-principles screening approaches exploiting energy trends in surface adsorption represent an unparalleled success story in recent computational catalysis research. Here we argue that our still limited understanding of the structure of active sites is one of the major bottlenecks towards an ever extended and reliable use of such computational screening for catalyst discovery. For low-index transition metal surfaces, the prevalently chosen high-symmetry (terrace and step) sites offered by the nominal bulk-truncated crystal lattice might be justified. For more complex surfaces and composite catalyst materials, computational screening studies will need to actively embrace a considerable uncertainty with respect to what truly are the active sites. By systematically exploring the space of possible active site motifs, such studies might eventually contribute towards a targeted design of optimized sites in future catalysts

Active discovery of organic semiconductors

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

Implicit Solvation Methods for Catalysis at Electrified Interfaces

Implicit solvation is an effective, highly coarse-grained approach in atomic-scale simulations to account for a surrounding liquid electrolyte on the level of a continuous polarizable medium. Originating in molecular chemistry with finite solutes, implicit solvation techniques are now increasingly used in the context of first-principles modeling of electrochemistry and electrocatalysis at extended (often metallic) electrodes. The prevalent ansatz to model the latter electrodes and the reactive surface chemistry at them through slabs in periodic boundary condition supercells brings its specific challenges. Foremost this concerns the difficulty of describing the entire double layer forming at the electrified solid–liquid interface (SLI) within supercell sizes tractable by commonly employed density functional theory (DFT). We review liquid solvation methodology from this specific application angle, highlighting in particular its use in the widespread ab initio thermodynamics approach to surface catalysis. Notably, implicit solvation can be employed to mimic a polarization of the electrode’s electronic density under the applied potential and the concomitant capacitive charging of the entire double layer beyond the limitations of the employed DFT supercell. Most critical for continuing advances of this effective methodology for the SLI context is the lack of pertinent (experimental or high-level theoretical) reference data needed for parametrization

Finding the Right Bricks for Molecular Legos: A Data Mining Approach to Organic Semiconductor Design

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 toward promising design strategies. Here, we demonstrate such data mining by clustering an in-house 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 side groups with which these molecular backbones are functionalized. In both cases, we obtain statistically significant structure–property relationships with certain scaffolds (side groups) consistently leading to favorable charge-transport properties. Functionalizing promising scaffolds with favorable side groups results in engineered molecular crystals for which we indeed compute improved charge-transport properties

Adaptive bill morphology for enhanced tool manipulation in New Caledonian crows

Early increased sophistication of human tools is thought to be underpinned by adaptive morphology for efficient tool manipulation. Such adaptive specialisation is unknown in nonhuman primates but may have evolved in the New Caledonian crow, which has sophisticated tool manufacture. The straightness of its bill, for example, may be adaptive for enhanced visually-directed use of tools. Here, we examine in detail the shape and internal structure of the New Caledonian crow’s bill using Principal Components Analysis and Computed Tomography within a comparative framework. We found that the bill has a combination of interrelated shape and structural features unique within Corvus, and possibly birds generally. The upper mandible is relatively deep and short with a straight cutting edge, and the lower mandible is strengthened and upturned. These novel combined attributes would be functional for (i) counteracting the unique loading patterns acting on the bill when manipulating tools, (ii) a strong precision grip to hold tools securely, and (iii) enhanced visually-guided tool use. Our findings indicate that the New Caledonian crow’s innovative bill has been adapted for tool manipulation to at least some degree. Early increased sophistication of tools may require the co-evolution of morphology that provides improved manipulatory skills

Thermal and electronic fluctuations of flexible adsorbed molecules : azobenzene on Ag(111)

We investigate the thermal and electronic collective fluctuations that contribute to the finite-temperature adsorption properties of flexible adsorbates on surfaces on the example of the molecular switch azobenzene C12H10N2 on the Ag(111) surface. Using first-principles molecular dynamics simulations, we obtain the free energy of adsorption that accurately accounts for entropic contributions, whereas the inclusion of many-body dispersion interactions accounts for the electronic correlations that govern the adsorbate binding. We find the adsorbate properties to be strongly entropy driven, as can be judged by a kinetic molecular desorption prefactor of 10^24 s−1 that largely exceeds previously reported estimates. We relate this effect to sizable fluctuations across structural and electronic observables. A comparison of our calculations to temperature-programed desorption measurements demonstrates that finite-temperature effects play a dominant role for flexible molecules in contact with polarizable surfaces, and that recently developed first-principles methods offer an optimal tool to reveal novel collective behavior in such complex systems