Population Balance Equations for Reactive Separation in Polymer Upcycling.

Many polymer upcycling efforts aim to convert plastic waste into high-value liquid hydrocarbons. However, the subsequent cleavage of middle distillates to light gases can be problematic. The reactor often contains a vapor phase (light gases and middle distillates) and a liquid phase (molten polymers and waxes with a suspended or dissolved catalyst). Because the catalyst resides in the liquid phase, middle distillates that partition into the vapor phase are protected against further cleavage into light gases. In this paper, we consider a simple reactive separation strategy, in which a gas outflow removes the volatile products as they form. We combine vapor-liquid equilibrium models and population balance equations (PBEs) to describe polymer upcycling in a two-phase semibatch reactor. The results suggest that the temperature, headspace volume, and flow rate of the reactor can be used to tune selectivity toward the middle distillates, in addition to the molecular mechanism of catalysis. We anticipate that two-phase reactor models will be important in many polymer upcycling processes and that reactive separation strategies will provide ways to boost the yield of the desired products in these cases.

Invariant Molecular Representations for Heterogeneous Catalysis.

Catalyst screening is a critical step in the discovery and development of heterogeneous catalysts, which are vital for a wide range of chemical processes. In recent years, computational catalyst screening, primarily through density functional theory (DFT), has gained significant attention as a method for identifying promising catalysts. However, the computation of adsorption energies for all likely chemical intermediates present in complex surface chemistries is computationally intensive and costly due to the expensive nature of these calculations and the intrinsic idiosyncrasies of the methods or data sets used. This study introduces a novel machine learning (ML) method to learn adsorption energies from multiple DFT functionals by using invariant molecular representations (IMRs). To do this, we first extract molecular fingerprints for the reaction intermediates and later use a Siamese-neural-network-based training strategy to learn invariant molecular representations or the IMR across all available functionals. Our Siamese network-based representations demonstrate superior performance in predicting adsorption energies compared with other molecular representations. Notably, when considering mean absolute values of adsorption energies as 0.43 eV (PBE-D3), 0.46 eV (BEEF-vdW), 0.81 eV (RPBE), and 0.37 eV (scan+rVV10), our IMR method has achieved the lowest mean absolute errors (MAEs) of 0.18 0.10, 0.16, and 0.18 eV, respectively. These results emphasize the superior predictive capacity of our Siamese network-based representations. The empirical findings in this study illuminate the efficacy, robustness, and dependability of our proposed ML paradigm in predicting adsorption energies, specifically for propane dehydrogenation on a platinum catalyst surface.

Benchmarking the Accuracy of Density Functional Theory against the Random Phase Approximation for the Ethane Dehydrogenation Network on Pt(111).

The Random Phase Approximation (RPA) is conceptually the most accurate Density Functional Approximation method, able to simultaneously predict both adsorbate and surface energies accurately; however, this work questions its superiority over DFT for catalytic application on hydrocarbon systems. This work uses microkinetic modeling to benchmark the accuracy of DFT functionals against that of RPA for the ethane dehydrogenation reaction on Pt(111). Eight different functionals, with and without dispersion corrections, across the GGA, meta-GGA and hybrid classes are evaluated: PBE, PBE-D3, RPBE, RPBE-D3, BEEF-vdW, SCAN, SCAN-rVV10, and HSE06. We show that PBE and RPBE, without dispersion correction, closely model RPA energies for adsorption, transition states, reaction, and activation energies. Next, RPA fails to describe the gas phase energy as unsaturation and chain-length increases in the hydrocarbon. Finally, we show that RPBE has the best accuracy-to-cost ratio, and RPA is likely not superior to RPBE or BEEF-vdW, which also gives a measure of uncertainty.

Unlocking the Potential of A-Site Ca-Doped LaCo0.2Fe0.8O3-δ: A Redox-Stable Cathode Material Enabling High Current Density in Direct CO2 Electrolysis.

Massive carbon dioxide (CO2) emission from recent human industrialization has affected the global ecosystem and raised great concern for environmental sustainability. The solid oxide electrolysis cell (SOEC) is a promising energy conversion device capable of efficiently converting CO2 into valuable chemicals using renewable energy sources. However, Sr-containing cathode materials face the challenge of Sr carbonation during CO2 electrolysis, which greatly affects the energy conversion efficiency and long-term stability. Thus, A-site Ca-doped La1-xCaxCo0.2Fe0.8O3-δ (0.2 ≤ x ≤ 0.6) oxides are developed for direct CO2 conversion to carbon monoxide (CO) in an intermediate-temperature SOEC (IT-SOEC). With a polarization resistance as low as 0.18 Ω cm2 in pure CO2 atmosphere, a remarkable current density of 2.24 A cm-2 was achieved at 1.5 V with La0.6Ca0.4Co0.2Fe0.8O3-δ (LCCF64) as the cathode in La0.8Sr0.2Ga0.83Mg0.17O3-δ (LSGM) electrolyte (300 µm) supported electrolysis cells using La0.6Sr0.4Co0.2Fe0.8O3-δ (LSCF) as the air electrode at 800 °C. Furthermore, symmetrical cells with LCCF64 as the electrodes also show promising electrolysis performance of 1.78 A cm-2 at 1.5 V at 800 °C. In addition, stable cell performance has been achieved on direct CO2 electrolysis at an applied constant current of 0.5 A cm-2 at 800 °C. The easily removable carbonate intermediate produced during direct CO2 electrolysis makes LCCF64 a promising regenerable cathode. The outstanding electrocatalytic performance of the LCCF64 cathode is ascribed to the highly active and stable metal/perovskite interfaces that resulted from the in situ exsolved Co/CoFe nanoparticles and the additional oxygen vacancies originated from the Ca2Fe2O5 phase synergistically providing active sites for CO2 adsorption and electrolysis. This study offers a novel approach to design catalysts with high performance for direct CO2 electrolysis.

Theoretical Investigation of the Electrochemical Oxidation of H2 and CO Fuels on a Ruddlesden-Popper SrLaFeO4-δ Anode.

The electrochemical oxidation of H2 and CO fuels have been investigated on the Ruddlesden-Popper layered perovskite SrLaFeO4-δ (SLF) under anodic solid oxide fuel cell conditions using periodic density functional theory and microkinetic modeling techniques. Two distinct FeO2-plane-terminated surface models differing in terms of the underlying rock salt layer (SrO or LaO) are used to identify the active site and limiting factors for the electro-oxidation of H2, CO, and syngas fuels. Microkinetic modeling predicted an order of magnitude higher turnover frequency for the electro-oxidation of H2 compared to CO for SLF at short-circuit conditions. The surface model with an underlying SrO layer was found to be more active with respect to H2 oxidation than the LaO-based surface model. At an operating voltage of less than 0.7 V, surface H2O/CO2 formation was found to be the key rate-limiting step, and the surface H2O/CO2 desorption was the key charge transfer step. In contrast, the bulk oxygen migration process was found to affect the overall rate at high cell voltage conditions above 0.9 V. In the presence of syngas fuel, the overall electrochemical activity is derived mainly from H2 electro-oxidation and CO2 is chemically shifted to CO via the reverse water-gas shift reaction. Substitutional doping of a surface Fe atom with Co, Ni, and Mn revealed that the H2 electro-oxidation activity of FeO2-plane terminated anodes with an underlying LaO rock salt layer can be improved with dopant introduction, with Co yielding a three orders of magnitude higher activity relative to the undoped LaO surface model. Constrained ab initio thermodynamic analysis furthermore suggested that the SLF anodes are resistant toward sulfur poisoning both in the presence and absence of dopants. Our findings reflect the role of various elements in controlling the fuel oxidation activity of SLF anodes that could aid the development of new Ruddlesden-Popper phase materials for fuel cell applications.

Liquid-Phase Effects on Adsorption Processes in Heterogeneous Catalysis.

Aqueous solvation free energies of adsorption have recently been measured for phenol adsorption on Pt(111). Endergonic solvent effects of ∼1 eV suggest solvents dramatically influence a metal catalyst's activity with significant implications for the catalyst design. However, measurements are indirect and involve adsorption isotherm models, which potentially reduces the reliability of the extracted energy values. Computational, implicit solvation models predict exergonic solvation effects for phenol adsorption, failing to agree with measurements even qualitatively. In this study, an explicit, hybrid quantum mechanical/molecular mechanical approach for computing solvation free energies of adsorption is developed, solvation free energies of phenol adsorption are computed, and experimental data for solvation free energies of phenol adsorption are reanalyzed using multiple adsorption isotherm models. Explicit solvation calculations predict an endergonic solvation free energy for phenol adsorption that agrees well with measurements to within the experimental and force field uncertainties. Computed adsorption free energies of solvation of carbon monoxide, ethylene glycol, benzene, and phenol over the (111) facet of Pt and Cu suggest that liquid water destabilizes all adsorbed species, with the largest impact on the largest adsorbates.

Unraveling Unique Surface Chemistry of Transition Metal Nitrides in Controlling Selective C-O Bond Scission Pathways of Glycerol.

Controlled C-O bond scission is an important step for upgrading glycerol, a major byproduct from the continuously increasing biodiesel production. Transition metal nitride catalysts have been identified as promising hydrodeoxygenation (HDO) catalysts, but fundamental understanding regarding the active sites of the catalysts and reaction mechanism remains unclear. This work demonstrates a fundamental surface science study of Mo2N and Cu/Mo2N for the selective HDO reaction of glycerol, using a combination of model surface experiments and first-principles calculations. Temperature-programmed desorption (TPD) experiments showed that clean Mo2N cleaved two or three C-O bonds of glycerol to produce allyl alcohol, propanal, and propylene. The addition of Cu to Mo2N changed the reaction pathway to one C-O bond scission to produce acetol. High-resolution electron energy loss spectroscopy (HREELS) results identified the surface intermediates, showing a facile C-H bond activation on Mo2N. Density functional theory (DFT) calculations revealed that the surface N on Mo2N interacted with the H atoms in glycerol and blocked some Mo sites to enable selective C-O bond scission. This work shows that Mo2N and Cu/Mo2N are active and selective for the controlled C-O bond scission of glycerol and in turn provides insights into the rational catalyst design for selective oxygen removal of relevant biomass-derived oxygenates.

Size-Controlled Nanoparticles Embedded in a Mesoporous Architecture Leading to Efficient and Selective Hydrogenolysis of Polyolefins.

A catalytic architecture, comprising a mesoporous silica shell surrounding platinum nanoparticles (NPs) supported on a solid silica sphere (mSiO2/Pt-X/SiO2; X is the mean NP diameter), catalyzes hydrogenolysis of melt-phase polyethylene (PE) into a narrow C23-centered distribution of hydrocarbons in high yield using very low Pt loadings (∼10-5 g Pt/g PE). During catalysis, a polymer chain enters a pore and contacts a Pt NP where the C-C bond cleavage occurs and then the smaller fragment exits the pore. mSiO2/Pt/SiO2 resists sintering or leaching of Pt and provides high yields of liquids; however, many structural and chemical effects on catalysis are not yet resolved. Here, we report the effects of Pt NP size on activity and selectivity in PE hydrogenolysis. Time-dependent conversion and yields and a lumped kinetics model based on the competitive adsorption of long vs short chains reveal that the activity of catalytic material is highest with the smallest NPs, consistent with a structure-sensitive reaction. Remarkably, the three mSiO2/Pt-X/SiO2 catalysts give equivalent selectivity. We propose that mesoscale pores in the catalytic architecture template the C23-centered distribution, whereas the active Pt sites influence the carbon-carbon bond cleavage rate. This conclusion provides a framework for catalyst design by separating the C-C bond cleavage activity at catalytic sites from selectivity for chain lengths of the products influenced by the structure of the catalytic architecture. The increased activity, selectivity, efficiency, and lifetime obtained using this architecture highlight the benefits of localized and confined environments for isolated catalytic particles under condensed-phase reaction conditions.

Probing surface-adsorbate interactions through active particle dynamics.

Adsorbate molecules present in a reaction mixture may bind to and block catalytic sites. Measurement of the surface coverage of these molecules via adsorption isotherms is critical for modeling and design of catalytic reactions on surfaces. However, it is challenging to measure isotherms in solution in a way that is directly relevant to catalytic activity under reaction conditions, particularly since adsorbates may bind with an enormous range of surface affinity parameters. Here we used the motion of self-propelled catalytic Janus particles, which employ the decomposition of hydrogen peroxide fuel as a propulsion mechanism, to determine the effective surface coverage of thioglycerol, furfural, and ethanol on a platinum surface as a function of concentration in aqueous solution by measuring the decrease in active motion due to the blocking of active sites. For strongly adsorbing thioglycerol, this effective coverage was compared and contrasted to the total adsorbed amount measured using inductively-coupled plasma analysis. Demonstrating the broad applicability of this approach, the surface affinity of the three adsorbates spanned more than four orders of magnitude. For each species, the adsorbate-mediated attenuation of active motion occurred over a wide concentration range and was well-described by a Langmuir isotherm. The strongly interacting thioglycerol had the highest affinity towards the surface (Ka = 15.5 ± 4.3 mM-1) and fully deactivated the active particle motion at surface saturation. Furfural had an intermediate affinity (Ka = 0.42 ± 0.07 mM-1) but did not fully block H2O2 access to the surface at apparent saturation, consistent with a maximum fractional surface coverage of θmax = 0.67. Ethanol exhibited even lower affinity (Ka = 0.0025 ± 2x10-4 mM-1) and its coverage saturated at only θmax = 0.38. Analysis of isotherms at elevated temperatures enabled direct extraction of the enthalpies of adsorption. The degree of surface coverage at adsorbate saturation appeared to correlate with the relative energies of adsorption for the different adsorbate species and was consistent with adsorbate saturation of one of multiple active site populations towards H2O2 decomposition. Moreover, computational investigations into solvent effects on furfural adsorption showed good quantitative agreement with the experimental results. This work leverages unique properties of active particles to explore fundamental catalysis questions and demonstrates a novel paradigm for significant and experimentally accessible multidisciplinary research.

A Multiple Filter Based Neural Network Approach to the Extrapolation of Adsorption Energies on Metal Surfaces for Catalysis Applications.

Computational catalyst discovery involves the development of microkinetic reactor models based on estimated parameters determined from density functional theory (DFT). For complex surface chemistries, the number of reaction intermediates can be very large, and the cost of calculating the adsorption energies by DFT for all surface intermediates even for one active site model can become prohibitive. In this paper, we have identified appropriate descriptors and machine learning models that can be used to predict a significant part of these adsorption energies given data on the rest of them. Moreover, our investigations also included the case when the species data used to train the predictive model are of different size relative to the species the model tries to predict-this is an extrapolation in the data space which is typically difficult with regular machine learning models. Due to the relative size of the available data sets, we have attempted to extrapolate from the larger species to the smaller ones in the current work. Here, we have developed a neural network based predictive model that combines an established additive atomic contribution based model with the concepts of a convolutional neural network that, when extrapolating, achieves a statistically significant improvement over the previous models.

Dependency of solvation effects on metal identity in surface reactions.

Solvent interactions with adsorbed moieties involved in surface reactions are often believed to be similar for different metal surfaces. However, solvents alter the electronic structures of surface atoms, which in turn affects their interaction with adsorbed moieties. To reveal the importance of metal identity on aqueous solvent effects in heterogeneous catalysis, we studied solvent effects on the activation free energies of the O-H and C-H bond cleavages of ethylene glycol over the (111) facet of six transition metals (Ni, Pd, Pt, Cu, Ag, Au) using an explicit solvation approach based on a hybrid quantum mechanical/molecular mechanical (QM/MM) description of the potential energy surface. A significant metal dependence on aqueous solvation effects was observed that suggests solvation effects must be studied in detail for every reaction system. The main reason for this dependence could be traced back to a different amount of charge-transfer between the adsorbed moieties and metals in the reactant and transition states for the different metal surfaces.

Upcycling Single-Use Polyethylene into High-Quality Liquid Products.

Our civilization relies on synthetic polymers for all aspects of modern life; yet, inefficient recycling and extremely slow environmental degradation of plastics are causing increasing concern about their widespread use. After a single use, many of these materials are currently treated as waste, underutilizing their inherent chemical and energy value. In this study, energy-rich polyethylene (PE) macromolecules are catalytically transformed into value-added products by hydrogenolysis using well-dispersed Pt nanoparticles (NPs) supported on SrTiO3 perovskite nanocuboids by atomic layer deposition. Pt/SrTiO3 completely converts PE (M n = 8000-158,000 Da) or a single-use plastic bag (M n = 31,000 Da) into high-quality liquid products, such as lubricants and waxes, characterized by a narrow distribution of oligomeric chains, at 170 psi H2 and 300 °C under solvent-free conditions for reaction durations up to 96 h. The binding of PE onto the catalyst surface contributes to the number averaged molecular weight (M n) and the narrow polydispersity (D) of the final liquid product. Solid-state nuclear magnetic resonance of 13C-enriched PE adsorption studies and density functional theory computations suggest that PE adsorption is more favorable on Pt sites than that on the SrTiO3 support. Smaller Pt NPs with higher concentrations of undercoordinated Pt sites over-hydrogenolyzed PE to undesired light hydrocarbons.

Controlling reaction pathways of selective C-O bond cleavage of glycerol.

The selective hydrodeoxygenation (HDO) reaction is desirable to convert glycerol into various value-added products by breaking different numbers of C-O bonds while maintaining C-C bonds. Here we combine experimental and density functional theory (DFT) results to reveal that the Cu modifier can significantly reduce the oxophilicity of the molybdenum carbide (Mo2C) surface and change the product distribution. The Mo2C surface is active for breaking all C-O bonds to produce propylene. As the Cu coverage increases to 0.5 monolayer (ML), the Cu/Mo2C surface shows activity towards breaking two C-O bonds and forming ally-alcohol and propanal. As the Cu coverage further increases, the Cu/Mo2C surface cleaves one C-O bond to form acetol. DFT calculations reveal that the Mo2C surface, Cu-Mo interface, and Cu surface are distinct sites for the production of propylene, ally-alcohol, and acetol, respectively. This study explores the feasibility of tuning the glycerol HDO selectivity by modifying the surface oxophilicity.

Electronic Properties of Bimetallic Metal-Organic Frameworks (MOFs): Tailoring the Density of Electronic States through MOF Modularity.

The development of porous well-defined hybrid materials (e.g., metal-organic frameworks or MOFs) will add a new dimension to a wide number of applications ranging from supercapacitors and electrodes to "smart" membranes and thermoelectrics. From this perspective, the understanding and tailoring of the electronic properties of MOFs are key fundamental challenges that could unlock the full potential of these materials. In this work, we focused on the fundamental insights responsible for the electronic properties of three distinct classes of bimetallic systems, Mx-yM'y-MOFs, MxM'y-MOFs, and Mx(ligand-M'y)-MOFs, in which the second metal (M') incorporation occurs through (i) metal (M) replacement in the framework nodes (type I), (ii) metal node extension (type II), and (iii) metal coordination to the organic ligand (type III), respectively. We employed microwave conductivity, X-ray photoelectron spectroscopy, diffuse reflectance spectroscopy, powder X-ray diffraction, inductively coupled plasma atomic emission spectroscopy, pressed-pellet conductivity, and theoretical modeling to shed light on the key factors responsible for the tunability of MOF electronic structures. Experimental prescreening of MOFs was performed based on changes in the density of electronic states near the Fermi edge, which was used as a starting point for further selection of suitable MOFs. As a result, we demonstrated that the tailoring of MOF electronic properties could be performed as a function of metal node engineering, framework topology, and/or the presence of unsaturated metal sites while preserving framework porosity and structural integrity. These studies unveil the possible pathways for transforming the electronic properties of MOFs from insulating to semiconducting, as well as provide a blueprint for the development of hybrid porous materials with desirable electronic structures.

Theoretical investigation of H2 oxidation on the Sr2Fe(1.5)Mo(0.5)O6 (001) perovskite surface under anodic solid oxide fuel cell conditions.

Periodic density functional theory (DFT) calculations and microkinetic modeling are used to investigate the electrochemical oxidation of H2 fuel on the (001) surface of Sr2Fe1.5Mo0.5O6 (SFMO) perovskite under anodic solid oxide fuel cell conditions. Three surface models with different Fe/Mo ratios in the topmost layer-identified by ab initio thermodynamic analysis-are used to investigate the H2 oxidation mechanism. A microkinetic analysis that considers the effects of anode bias potential suggests that a higher Mo concentration in the surface increases the activity of the surface toward H2 oxidation. At operating voltage and anodic SOFC conditions, the model predicts that water desorption is rate-controlling and that stabilizing the oxygen vacancy structure increases the overall rate for H2 oxidation. Although we find that Mo plays a crucial role in improving catalytic activity of SFMO, under fuel cell operating conditions, the Mo content in the surface layer tends to be very low. On the basis of these results and in agreement with previous experimental observations, a strategy for improving the overall electrochemical performance of SFMO is increasing the Mo content or adding small amounts of an active transition metal, such as Ni, to the surface to lower the oxygen vacancy formation energy of the SFMO surface.

Adaptive-Partitioning QM/MM Dynamics Simulations: 3. Solvent Molecules Entering and Leaving Protein Binding Sites.

The adaptive-partitioning (AP) schemes for combined quantum-mechanical/molecular-mechanical (QM/MM) calculations allow on-the-fly reclassifications of atoms and molecules as QM or MM in dynamics simulations. The permuted-AP (PAP) scheme (J. Phys. Chem. B 2007, 111, 2231) introduces a thin layer of buffer zone between the QM subsystem (also called active zone) and the MM subsystem (also known as the environmental zone) to provide a continuous and smooth transition and expresses the potential energy in a many-body expansion manner. The PAP scheme has been successfully applied to study small molecules solvated in bulk solvent. Here, we propose two modifications to the original PAP scheme to treat solvent molecules entering and leaving protein binding sites. First, the center of the active zone is placed at a pseudoatom in the binding site, whose position is not affected by the movements of ligand or residues in the binding site. Second, the extra forces due to the smoothing functions are deleted. The modified PAP scheme no longer describes a Hamiltonian system, but it satisfies the conservation of momentum. As a proof-of-concept experiment, the modified PAP scheme is applied to the simulations under the canonical ensemble for two binding sites of the Escherichia coli CLC chloride ion transport protein, in particular, the intracellular binding site Sint discovered by crystallography and one putative additional binding site Sadd suggested by molecular modeling. The exchange of water molecules between the binding sites and bulk solvent is monitored. For comparison, simulations are also carried out using the same model system and setup with only one exception: the extra forces due to the smoothing functions are retained. The simulations are benchmarked against conventional QM/MM simulations with large QM subsystems. The results demonstrate that the active zone centered at the pseudo atom is a reasonable and convenient representation of the binding site. Moreover, the transient extra forces are non-negligible and cause the QM water molecules to move out of the active zone. The modified PAP scheme, where the extra forces are excluded, avoids the artifact, providing a realistic description of the exchange of water molecules between the protein binding sites and bulk solvent.

Hybrid Quantum Mechanics/Molecular Mechanics Solvation Scheme for Computing Free Energies of Reactions at Metal-Water Interfaces.

We report the development of a quantum mechanics/molecular mechanics free energy perturbation (QM/MM-FEP) method for modeling chemical reactions at metal-water interfaces. This novel solvation scheme combines planewave density function theory (DFT), periodic electrostatic embedded cluster method (PEECM) calculations using Gaussian-type orbitals, and classical molecular dynamics (MD) simulations to obtain a free energy description of a complex metal-water system. We derive a potential of mean force (PMF) of the reaction system within the QM/MM framework. A fixed-size, finite ensemble of MM conformations is used to permit precise evaluation of the PMF of QM coordinates and its gradient defined within this ensemble. Local conformations of adsorbed reaction moieties are optimized using sequential MD-sampling and QM-optimization steps. An approximate reaction coordinate is constructed using a number of interpolated states and the free energy difference between adjacent states is calculated using the QM/MM-FEP method. By avoiding on-the-fly QM calculations and by circumventing the challenges associated with statistical averaging during MD sampling, a computational speedup of multiple orders of magnitude is realized. The method is systematically validated against the results of ab initio QM calculations and demonstrated for C-C cleavage in double-dehydrogenated ethylene glycol on a Pt (111) model surface.

Modeling the noble metal/TiO2 (110) interface with hybrid DFT functionals: a periodic electrostatic embedded cluster model study.

The interaction of Au(n) and Pt(n) (n=2,3) clusters with the stoichiometric and partially reduced rutile TiO(2) (110) surfaces has been investigated using periodic slab and periodic electrostatic embedded cluster models. Compared to Au clusters, Pt clusters interact strongly with both stoichiometric and reduced TiO(2) (110) surfaces and are able to enhance the reducibility of the TiO(2) (110) surface, i.e., reduce the oxygen vacancy formation energy. The focus of this study is the effect of Hartree-Fock exchange on the description of the strength of chemical bonds at the interface of Au/Pt clusters and the TiO(2) (110) surface. Hartree-Fock exchange helps describing the changes in the electronic structures due to metal cluster adsorption as well as their effect on the reducibility of the TiO(2) surface. Finally, the performance of periodic embedded cluster models has been assessed by calculating the Pt adsorption and oxygen vacancy formation energies. Cluster models, together with hybrid PBE0 functional, are able to efficiently compute reasonable electronic structures of the reduced TiO(2) surface and predict charge localization at surface oxygen vacancies, in agreement with the experimental data, that significantly affect computed adsorption and reaction energies.

Conservative Algorithm for an Adaptive Change of Resolution in Mixed Atomistic/Coarse-Grained Multiscale Simulations.

We derive a Hamiltonian and present a simulation protocol for mixed-resolution systems that allows for a change in resolution of selected groups of atoms during a molecular dynamics simulation. The Hamiltonian uses a low-resolution force field for the part of the system distant from an active site (for efficiency) and an atomistic force field for the active site and its direct environment (for accuracy). A microcanonical simulation protocol conserves energy and angular and linear momentum. The method is also applicable to simulations in other ensembles.

Tight-Binding Configuration Interaction (TBCI): A Noniterative Approach to Incorporating Electrostatics into Tight Binding.

We present a new electronic structure approximation called Tight Binding Configuration Interaction. It uses a tight-binding Hamiltonian to obtain orbitals that are used in a configuration interaction calculation that includes explicit charge interactions. This new method is better capable of predicting energies, ionization potentials, and fragmentation charges than the Wolfsberg-Helmholz Tight-Binding and Many-Body Tight-Binding models reported earlier (Staszewska, G.; Staszewski, P.; Schultz, N. E.; Truhlar, D. Phys. Rev. B 2005, 71, 045423). The method is illustrated for clusters and nanoparticles containing aluminum.