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    The principal effort of our research program is in the design of new functional materials (heterogeneous and homogeneous catalysts, artificial metalloenzymes, optically special quantum dots, ultra hard alloys, quantum solids, qubits and their assemblies), guided by insights into electronic structure and chemical bonding. Since we are interested in materials' functions, we model them in a maximally realitic way, representing the conditions of practical use, and accounting for dynamics, complex effects of temperature, pressure, or mechanical stress. We marry catalysis to statistical mechanics, enzymology to quantum chemistry, quantum computing and alloy design to chemical bonding, and all of these to novel algorithms that we develop. We use a large range of methods (DFT, ab initio, QM/MM, MD, MC, novel reaction path samplings, global optimizations, etc), and often make them multi-scale, combining several different approaches, and powering them by tricks from artificial intelligence and machine learning. Both applied and method-development efforts are prominent in the group, and we are a warm home for students of many different backgrounds, from chemistry and biochemistry, to physics, material science and engineering, computer science, and applied mathematics. We collaborate with many experimental laboratories in catalysis, surface science, spectroscopy, and molecular biology.

    Over the years, this research has been powered by:
    Supported clusters and nanoparticles. Fluxionality. (Photo-)electrocatalysis. Heterogeneous catalytic interfaces set in motion by reaction conditions.

    Our interest is in dynamic heterogeneous catalytic interfaces, for which we discovered a new paradigm breaking many textbook rules of catalysis, and requiring new theories and algorithms for realistic modeling. In particular, semiconductor supports decorated with sub-nano clusters of transition metals are of interest. Our group pioneered the ensemble representaiton of cluster-based catalysts: because clusters in the presence of adsorbates and at increased temperatures are dynamic, fluxional, and adopt a variety of shapes, many of these states are simultaneously populated in reaction conditiosn. Hence, the catalytic activity, selectivity, spectral characteristics, deactivation propentity, and every property indeed are ensemble-averages. As a result, many traditional rules of catalysis get broken, such as scaling relations. Importantly, ofte, it is not the most stable, but the less stable and yet thermally accessible cluster isomers are responsible for the majority of the catalytic effect. Other types of functional surfaces where this new paradigm holds up include electrocatalytic interfaces, corroding surfaces, and any kind of surface prone to the formation of defects, and reconstructions in conditions of practical use. We describe such interfaces using novel state-of-the-art algorithms that include electronic structure calculations, statistical mechanics, and elements of machine learning (such as Neural Networks). Most projects in this area are collaborative with experimentlists in surface science, catalysis, and operando spectroscopy.

    Selected References:

    Zandkarimi, B.; Alexandrova, A. N. Dynamic subnanometer Pt clusters can break the scaling relationships in catalysis. 2019, J. Phys. Chem. Lett., 10, accepted.

    Zhang, Z.; Jimenez-Izal, E.; Alexandrova, A. N. Dynamic Phase Diagram of Catalytic Surface of Hexagonal Boron Nitride in Conditions of Oxidative Dehydrogenation of Propane. 2019, J. Phys. Chem. Lett., 10, 20-25.

    Ha, M.-A.; Baxter, E. T.; Cass, A. C.; Anderson, S. L.; Alexandrova, A. N. Boron Switch for Selectivity of Catalytic Dehydrogenation on Size-Selected Pt clusters on Al2O3. 2017, J. Am. Chem. Soc., 139, 11568-11575.

    Baxter, E. T.; ha, M.-A.; Cass, A. C.; Alexandrova, A. N.; Anderson, S. L. Ethylene Dehydrogenaiton of Pt4,7,8 clusters on Al2O3: Strong Cluster-Size Dependence Linked to Preferred Catalyst Morphologies. 2017, ACS Catal, 7, 3322-3335.

    Zhai, H.; Alexandrova, A. N. Fluxionality of Catalytic Clusters: When It Matters and How to Address It. 2017, ACS Catal., 7, 1905-1911.

    Zhai, H.; Alexandrova, A. N. Ensemble-Average Representaiton of Pt Clusters in Conditions of Catalysis Accessed through GPU Accelerated Deep Neural Network Fitting Globa Optimization. 2016, J. Chem. Theor. Comput., 12, 6213-6226.

    Ha, M.-A.; Dadras, J.; Alexandrova, A. N.* Rutile-supported Pt-Pd clusters: a hypothesis regarding the stability at 50/50 ratio. 2014, ACS Catal., Special issue on computational catalysis, 4, 3570-3580.

    Chemical bonding in materials, and materials design

    We develop the fundamental theory of chemical bonding for materials, both in 2-D and in 3-D, with the aim of rationalizaiton of their structures and properties, and eventual design that would not involve screenings or machine learning, but rather would be guided by the principles of electronic structure. Of interest are ultra-hard alloys (collaboraiton with Profs. Sarah Tolbert, Richard Kaner in UCLA), strongly-correlated materials (collaboration with Profs. McQueen and Bowen in Hopkins), new 2-D materials with interesting electronic and magnetic properties.

    Selected References:

    Robinson, P. J.; Liu, G.; Ciborowski, S.; Martinez-Martinez, C.; Chamorro, J.; McQueen, T. M.; Bowen, K. H.; Alexandrova, A. N. Mystery of Three Borides: Differential Metal-Boron Bonding Governing Superhard Structures. 2017, Chem. Mater., DOI: 10.1021/acs.chemmater.7b04378.

    Alexandrova, A. N. Divide-and-Conquer Chemical Bonding Models for Materials: a Tool for Materials Design at the Electronic Level. 2017, Chem. Mater., 29, 8555-8565.

    Cui, Z.; Jimenez-Izal, E.; Alexandrova, A. N. Prediciton of Two-Dimensional Phase of Boron with Anisotropic Electric Conductivitiy. 2017, J. Phys. Chem. Lett., 8, 1224-1228.

    Jimenez-Izal, E.; Saeys, M.; Alexandrova, A. N. Metallic and Magnetic 2D Materials Containing Planar Tetracoordinated C and N. 2016, J. Phys. Chem. C., 120, 21685-21690.

    Nandula, A.; Trinh, Q. T.; Saeys, M.; Alexandrova, A. N. Origin of Extraordinary Stability of Square Planar Carbon in Surface Carbides of Co, Ni, and Beyond. 2015, Angew. Chem. Int. Ed., VIP paper, 54,5312-5316.

    Enzymes as molecular capacitors: role of electric fields in enzymatic catalysis. Design of artificial metalloenzymes.

    Large protein macromolecules appear to exert meaningful intramolecular electric fields on the enzyme active sites. These fields contribute to activity and selectivity. We study these fields via quantum mechanically rigorous approaches, elucidate their specific roles in catalysis, and incorporate fields on purpose, via mutagenesis, to introduce or alter enzymatic functionality.

    We aim at designing catalysts that mimic natural enzymes in catalytic strategies, but catalyze reactions that interest humankind. This effort is presently fundamental. However, eventually, it might lead to unprecidented catalytic processes, green, efficient, and inexpensive, used at an industrial scale. The philosophy of this area of research is that the effort is largely done in silico, and experiment is envoked only at the end of the workflow to check theoretical predictions. Modeling of enzymes is done with atomistic and electronic insight. We develop fast techniques for mixed quantum-classical simulations and design. A particular interest is in metalloenzymes, which may containin non-physiological metals of the highest catalytic potency. At the moment we are particularly ponder the quenstion of electrostatic preorganization as one of the major driving forces behind the catalytic power of enzymes, and ways to incorportae it in our design protocols.

    Selected References:

    Morgensetern, A.; Jaszai, M.; Eberhart, M. E.; Alexandrova, A. N. Quantifying Electrostatic Preorganization in Enzymes Using the Geometry of Charge Density. 2017, Chem. Sci., 8, 5010-5018.

    Valdez, C. E.; Morgenstern, A.; Eberhart, M. E.; Alexandrova, A. N. Predictive Methods for Computational Metalloenzyme Reddesign - A Test Case with Carboxypeptidase A. 2016, Phys. Chem. Chem. Phys., 18, 31744-31756.

    Nechay, M. R.; Valdez, C. E.; Alexandrova, A. N. Computational Treatment of Metalloproteins. 2015, J. Phys. Chem. B, 119, 5945-5956. Feature Article.

    Valdez, C. E.; Smith, Q. A.; Nechay, M. W.; Alexandrova, A. N.* Mysteries of metals in metalloenzymes. 2014, Acc. Chem. Res., 47, 3110-3117.

    Sparta, M.; Valdez, C. E.; Alexandrova, A. N.* Metal-dependent activity of Fe and Ni acireductone dioxygenases: how two electrons reroute the catalytic pathway. 2013 J. Mol. Biol., 245, 3007-3018. Featured on the Cover

    Sparta, M.; Ding, F.; Shirvanyants, D.; Dokholyan, N. V.;* Alexandrova, A. N.* Hybrid dynamics simula tion engine for metalloproteins. 2012 Biophys. J. 103, 767-776. PDF

    Valdez, C. A.; Alexandrova, A. N.* Why Urease is a di-Nickel Enzyme, whereas the CcrA beta-Lactamase is a di-Zinc Enzyme. 2012 J. Phys. Chem. B., DOI: 10.1021/jp302771n. PDF

    Shirvanyants, D.; Alexandrova, A. N.; Dokholyan, N. V.* Rigid substructure search, 2011, Bioin formatics, 27, 1327-1329. PDF

    Molecular qubits for quantum computing

    In collaboration with physics groups of Wes Campbell and Eric Hudson, within the Quantum Computing initiative in UCLA and beyond, we help developing small molecular radicals that could work as qubits in quantum computers. Our role is electronic structure calculations of the electronic transitions in these radicals, and design of new qubit radicals that would have very vertical electronic excitaitons not easile smeared by coupling to vibrations or other electronic states, to be read as "0" and "1" in a quantum computer. We use wave function based methods with static and dynamic electron correlation and intense treatment of relativistic effects in this project. See the recent news artile about our team: UCLA Newsroom.