benchmark database for adsorption bond energies to transition metal surfaces and comparison to selected DFT functionals, Surface Science (2015),
ABSTRACTWe present a literature collection of experimental adsorption energies over late transition metal surfaces for systems where we believe the energy measurements are particularly accurate, and the atomic-scale adsorption geometries are particularly well established. We propose that this could become useful for benchmarking theoretical methods for calculating adsorption processes. We compare the experimental results to six commonly used electron density functionals, including some (RPBE, BEEF-vdW) which were specifically developed to treat adsorption processes. The comparison shows that there is ample room for improvements in the theoretical descriptions.
Hybrid quantum-classical algorithms provide ways to use noisy intermediate-scale quantum computers for practical applications. Expanding the portfolio of such techniques, we propose a quantum circuit learning algorithm that can be used to assist the characterization of quantum devices and to train shallow circuits for generative tasks. The procedure leverages quantum hardware capabilities to its fullest extent by using native gates and their qubit connectivity. We demonstrate that our approach can learn an optimal preparation of the Greenberger-Horne-Zeilinger states, also known as "cat states". We further demonstrate that our approach can efficiently prepare approximate representations of coherent thermal states, wave functions that encode Boltzmann probabilities in their amplitudes. Finally, complementing proposals to characterize the power or usefulness of near-term quantum devices, such as IBM's quantum volume, we provide a new hardware-independent metric called the qBAS score. It is based on the performance yield in a specific sampling task on one of the canonical machine learning data sets known as Bars and Stripes. We show how entanglement is a key ingredient in encoding the patterns of this data set; an ideal benchmark for testing hardware starting at four qubits and up. We provide experimental results and evaluation of this metric to probe the trade off between several architectural circuit designs and circuit depths on an ion-trap quantum computer.
Herein
we describe the C–O cleavage of phenol and cyclohexanol
over Rh(111) and Rh(211) surfaces using density functional theory
calculations. Our analysis is complemented by a microkinetic model
of the reactions, which indicates that the C–O bond cleavage
of cyclohexanol is easier than that of phenol and that Rh(211) is
more active than Rh(111) for both reactions. This indicates that phenol
will react mainly following a pathway of initial hydrogenation to
cyclohexanol followed by hydrodeoxygenation to cyclohexane. We show
that there is a general relationship between the transition state
and the final state of both C–O cleavage reactions, and that
this relationship is the same for Rh(111) and Rh(211).
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