We demonstrate that the reduction of p-nitrophenol to p-aminophenol by NaBH4 is catalyzed by both monometallic and bimetallic nanoparticles (NPs). We also demonstrate a straightforward and precise method for the synthesis of bimetallic nanoparticles using poly(amido)amine dendrimers. The resulting dendrimer encapsulated nanoparticles (DENs) are monodisperse, and the size distribution does not vary with different elemental combinations. Random alloys of Pt/Cu, Pd/Cu, Pd/Au, Pt/Au, and Au/Cu DENs were synthesized and evaluated as catalysts for p-nitrophenol reduction. These combinations are chosen in order to selectively tune the binding energy of the p-nitrophenol adsorbate to the nanoparticle surface. Following the Brønsted–Evans–Polanyi (BEP) relation, we show that the binding energy can reasonably predict the reaction rates of p-nitrophenol reduction. We demonstrate that the measured reaction rate constants of the bimetallic DENs is not always a simple average of the properties of the constituent metals. In particular, DENs containing metals with similar lattice constants produce a binding energy close to the average of the two constituents, whereas DENs containing metals with a lattice mismatch show a bimodal distribution of binding energies. Overall, in this work we present a uniform method for synthesizing pure and bimetallic DENs and demonstrate that their catalytic properties are dependent on the adsorbate’s binding energy.

We present a hybrid density functional theory (DFT) study of doping effects in α-Fe(2)O(3), hematite. Standard DFT underestimates the band gap by roughly 75% and incorrectly identifies hematite as a Mott-Hubbard insulator. Hybrid DFT accurately predicts the proper structural, magnetic, and electronic properties of hematite and, unlike the DFT+U method, does not contain d-electron specific empirical parameters. We find that using a screened functional that smoothly transitions from 12% exact exchange at short ranges to standard DFT at long range accurately reproduces the experimental band gap and other material properties. We then show that the antiferromagnetic symmetry in the pure α-Fe(2)O(3) crystal is broken by all dopants and that the ligand field theory correctly predicts local magnetic moments on the dopants. We characterize the resulting band gaps for hematite doped by transition metals and the p-block post-transition metals. The specific case of Pd doping is investigated in order to correlate calculated doping energies and optical properties with experimentally observed photocatalytic behavior.

The nature of the electronic ground state of the tetramethyleneethane (TME) diradical has proven to be a challenge for both experiment and theory. Through the use of quantum Monte Carlo (QMC) methods and multireference perturbation theory, we demonstrate that the lowest singlet state of TME is energetically lower than the lowest triplet state at all values of the torsional angle between the allyl subunits. Moreover, we find that the maximum in the potential energy curve for the singlet state occurs at a torsional angle near 45°, in contrast to previous calculations that placed the planar structure of the singlet state as the highest in energy. We also show that the CASPT2 method when used with a sufficiently large reference space and a sufficiently flexible basis set gives potential energy curves very close to those from the QMC calculations. Our calculations have converged the singlet-triplet gap of TME as a function of methodology and basis set. These results provide insight into the level of theory required to properly model diradicals, in particular disjoint diradicals, and provide guidelines for future studies on more complicated diradical systems.

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We present a method for optimizing transition state theory dividing surfaces with support vector machines. The resulting dividing surfaces require no a priori information or intuition about reaction mechanisms. To generate optimal dividing surfaces, we apply a cycle of machine-learning and refinement of the surface by molecular dynamics sampling. We demonstrate that the machine-learned surfaces contain the relevant low-energy saddle points. The mechanisms of reactions may be extracted from the machine-learned surfaces in order to identify unexpected chemically relevant processes. Furthermore, we show that the machine-learned surfaces significantly increase the transmission coefficient for an adatom exchange involving many coupled degrees of freedom on a (100) surface when compared to a distance-based dividing surface.