Publications

Molecular adsorption on surfaces plays an important part in catalysis, corrosion, desalination, and various other processes that are relevant to industry and in nature. As a complement to experiments, accurate adsorption energies can be obtained using various sophisticated electronic structure methods that can now be applied to periodic systems. The adsorption energy of water on boron nitride substrates, going from zero to 2-dimensional periodicity, is particularly interesting as it calls for an accurate treatment of polarizable electrostatics and dispersion interactions, as well as posing a practical challenge to experiments and electronic structure methods. Here, we present reference adsorption energies, static polarizabilities, and dynamic polarizabilities, for water on BN substrates of varying size and dimension. Adsorption energies are computed with coupled cluster theory, fixed-node quantum Monte Carlo (FNQMC), the random phase approximation, and second order Møller-Plesset theory. These wavefunction based correlated methods are found to agree in molecular as well as periodic systems. The best estimate of the water/h-BN adsorption energy is −107±7 meV from FNQMC. In addition, the water adsorption energy on the BN substrates could be expected to grow monotonically with the size of the substrate due to increased dispersion interactions, but interestingly, this is not the case here. This peculiar finding is explained using the static polarizabilities and molecular dispersion coefficients of the systems, as computed from time-dependent density functional theory (DFT). Dynamic as well as static polarizabilities are found to be highly anisotropic in these systems. In addition, the many-body dispersion method in DFT emerges as a particularly useful estimation of finite size effects for other expensive, many-body wavefunction based methods.

We revisit the long-standing question of whether water molecules dissociate on the Ru(0001) surface through nanosecond-scale path-integral molecular dynamics simulations on a sizable supercell. This is made possible through the development of an efficient and reliable machine-learning potential with near first-principles accuracy, overcoming the limitations of previous ab initio studies. We show that the quantum delocalization associated with nuclear quantum effects enables rapid and frequent proton transfers between water molecules, thereby facilitating the water dissociation on Ru(0001). This work provides the direct theoretical evidence of water dissociation on Ru(0001), resolving the enduring issue in surface sciences and offering crucial atomistic insights into water-metal interfaces.

No abstract is provided for this article.

Wet carbon interfaces are ubiquitous in the natural world and exhibit anomalous properties, which could be exploited by emerging technologies. However, progress is limited by lack of understanding at the molecular level. Remarkably, even for the most fundamental system (a single water molecule interacting with graphene), there is no consensus on the nature of the interaction. We tackle this by performing an extensive set of complementary state-of-the-art computer simulations on some of the world's largest supercomputers. From this effort a consensus on the water–graphene interaction strength has been obtained. Our results have significant impact for the physical understanding, as they indicate that the interaction is weaker than predicted previously. They also pave the way for more accurate and reliable studies of liquid water at carbon interfaces.

While often considered to be chemically inert, the reactivity of noble gas elements at elevated pressures is an important aspect of fundamental chemistry. The discovery of Xe oxidation transformed the doctrinal boundary of chemistry by showing that a complete electron shell is not inert to reaction. However, the reductive propensity, i.e., gaining electrons and forming anions, has not been proposed or examined for noble gas elements. In this work, we demonstrate, using first-principles electronic structure calculations coupled to an efficient structure prediction method, that Xe, Kr, and Ar can form thermodynamically stable compounds with Mg at high pressure (≥125, ≥250, and ≥250 GPa, respectively). The resulting compounds are metallic and the noble gas atoms are negatively charged, suggesting that chemical species with a completely filled shell can gain electrons, filling their outermost shell(s). Moreover, this work indicates that Mg2NG (NG = Xe, Kr, Ar) are high-pressure electrides with some of the electrons localized at interstitial sites enclosed by the surrounding atoms. Previous predictions showed that such electrides only form in Mg and its compounds at very high pressures (>500 GPa). These calculations also demonstrate strong chemical interactions between the Xe 5d orbitals and the quantized interstitial quasiatom (ISQ) orbitals, including the strong chemical bonding and electron transfer, revealing the chemical nature of the ISQ.

No abstract is provided for this article.

No abstract is provided for this article.

In a recent work, van Setten and coworkers have presented a carefully converged $G_0W_0$ study of 100 closed shell molecules [J. Chem. Theory Comput. 11, 5665 (2015)]. For two different codes they found excellent agreement to within few 10 meV if identical Gaussian basis sets were used. We inspect the same set of molecules using the projector augmented wave method and the Vienna ab initio simulation package (VASP). For the ionization potential, the basis set extrapolated plane wave results agree very well with the Gaussian basis sets, often reaching better than 50 meV agreement. In order to achieve this agreement, we correct for finite basis set errors as well as errors introduced by periodically repeated images. For electron affinities below the vacuum level differences between Gaussian basis sets and VASP are slightly larger. We attribute this to larger basis set extrapolation errors for the Gaussian basis sets. For quasi particle (QP) resonances above the vacuum level, differences between VASP and Gaussian basis sets are, however, found to be substantial. This is tentatively explained by insufficient basis set convergence of the Gaussian type orbital calculations as exemplified for selected test cases.

In order for methods combining ab initio density-functional theory and many-body techniques to become routinely used, a flexible, fast, and easy-to-use implementation is crucial. We present an implementation of a general charge self-consistent scheme based on projected localized orbitals in the projector augmented wave framework in the Vienna Ab Initio Simulation Package. We give a detailed description on how the projectors are optimally chosen and how the total energy is calculated. We benchmark our implementation in combination with dynamical mean-field theory: first we study the charge-transfer insulator NiO using a Hartree–Fock approach to solve the many-body Hamiltonian. We address the advantages of the optimized against non-optimized projectors and furthermore find that charge self-consistency decreases the dependence of the spectral function—especially the gap—on the double counting. Second, using continuous-time quantum Monte Carlo we study a monolayer of SrVO3, where strong orbital polarization occurs due to the reduced dimensionality. Using total-energy calculation for structure determination, we find that electronic correlations have a non-negligible influence on the position of the apical oxygens, and therefore on the thickness of the single SrVO3 layer.

CO adsorption on a PtCo(111) surface was studied by scanning tunneling microscopy. Comparison of images with chemical contrast of Pt and Co and images showing the CO molecules indicates that CO resides exclusively on top of Pt sites and never on Co. CO bonding is highly sensitive to the chemical environment. The probability to find CO on a Pt atom increases drastically with the number of its Co nearest neighbors. Ab initio calculations show that this ligand effect is due to different positions of the center of the Pt d band.

Realistic finite temperature simulations of matter are a formidable challenge for first principles methods. Long simulation times and large length scales are required, demanding years of compute time. Here we present an on-the-fly machine learning scheme that generates force fields automatically during molecular dynamics simulations. This opens up the required time and length scales, while retaining the distinctive chemical precision of first principles methods and minimizing the need for human intervention. The method is widely applicable to multi-element complex systems. We demonstrate its predictive power on the entropy driven phase transitions of hybrid perovskites, which have never been accurately described in simulations. Using machine learned potentials, isothermal-isobaric simulations give direct insight into the underlying microscopic mechanisms. Finally, we relate the phase transition temperatures of different perovskites to the radii of the involved species, and we determine the order of the transitions in Landau theory.