We present a method for real-time propagation of electronic wave functions, within time-dependent density functional theory (RT-TDDFT), coupled to ionic motion through mean-field classical dynamics. The goal of our method is to treat large systems and complex processes, in particular photocatalytic reactions and electron transfer events on surfaces and thin films. Due to the complexity of these processes, computational approaches are needed to provide insight into the underlying physical mechanisms and are therefore crucial for the rational design of new materials. Because of the short time step required for electron propagation (of order ∼10 attoseconds), these simulations are computationally very demanding. Our methodology is based on numerical atomic-orbital-basis sets for computational efficiency. In the computational package, to which we refer as TDAP-2.0 (Time-evolving Deterministic Atom Propagator), we have implemented a number of important features and analysis tools for more accurate and efficient treatment of large, complex systems and time scales that reach into a fraction of a picosecond. We showcase the capabilities of our method using four different examples: (i) photodissociation into radicals of opposite spin, (ii) hydrogen adsorption on aluminum surfaces, (iii) optical absorption of spin-polarized organic molecule containing a metal ion, and (iv) electron transfer in a prototypical dye-sensitized solar cell.

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Oxidative methanol dehydrogenation is a major industrial reaction with global formaldehyde production exceeding 30 million tonnes per year. Unfortunately, oxidative dehydrogenation produces water–aldehyde mixtures that require subsequent distillation. Anhydrous alcohol dehydrogenation is a promising alternative that produces H2 instead of water. Pursuant to recent experimental work showing that highly stepped Cu(111) surfaces exhibit anhydrous dehydrogenation activity, we present first-principles density functional theory calculations for methanol and ethanol dehydrogenation at Cu(111) step edges to provide an atomistic understanding of the catalytic mechanism; these sites stabilize all intermediates while reducing activation energies. We find that van der Waals contributions to the energy account for more than 50% of adsorption energies, and their inclusion is essential in achieving good agreement with experimental desorption temperatures. Furthermore, vibrational zero-point energy corrections significantly reduce the activation energy for all reaction steps considered here. Hydrogen bonding among ethanol intermediates at step edges is weakened by geometric frustration. These insights lead us to propose several suggestions for further research on undercoordinated Cu sites as anhydrous alcohol dehydrogenation catalysts.

Adsorption energies on surfaces are excellent descriptors of their chemical properties, including their catalytic performance. High-throughput adsorption energy predictions can therefore help accelerate first-principles catalyst design. To this end, we present over 5000 DFT calculations of H adsorption energies on dilute Ag alloys and describe a general machine learning approach to rapidly predict H adsorption energies for new Ag alloy structures. We find that random forests provide accurate predictions and that the best features are combinations of traditional chemical and structural descriptors. Further analysis of our model errors and the underlying forest kernel reveals unexpected finite-size electronic structure effects: embedded dopant atoms can display counterintuitive behavior such as nonmonotonic trends as a function of composition and high sensitivity to dopants far from the adsorbing H atom. We explain these behaviors with simple tight-binding Hamiltonians and d-orbital densities of states. We also use variations among forest leaves to predict the uncertainty of predictions, which allows us to mitigate the effects of larger errors.

Iodine-doped graphene has recently attracted significant interest as a result of its enhanced conductivity and improved catalytic activity. Using density functional theory calculations, we obtain the formation energy, desorption rate, and electronic properties for graphene systems doped with polyiodide chains consisting of 1–6 iodine atoms in the low-concentration limit. We find that I3 and I5 act as p-type surface dopants that shift the Fermi level 0.46 and 0.57 eV below the Dirac point, respectively. For these two molecules, molecular orbital theory and analysis of the charge density show that doping transfers electronic charge to iodine π* molecular orbitals oriented perpendicular to the graphene sheet. For even-length polyiodides, we find that I6 and I4 decompose to I2, which readily desorbs at 300 K. Adsorption energy calculations further show that I3 acts as an effective catalyst for the oxygen reduction reaction on graphene by stabilizing the rate-limiting OOH intermediate.