Liquid-drop model for crystalline metals: Vacancy-formation, cohesive, and face-dependent surface energies

Spectroscopic Probe Molecule Selection Using Quantum Theory, First-Principles Calculations, and Machine Learning

Probe molecule vibrational spectra have a long history of being used to characterize materials including metals, oxides, metal-organic frameworks, and even human proteins. Furthermore, recent advances in machine learning have enabled computationally generated spectra to aid in detailed characterization of complex surfaces with probe molecules. Despite widespread use of probe molecules, the science of probe molecule selection is underdeveloped. Here, we develop physical concepts, including orbital interaction energy and the energy overlap integral, to explain and predict the ability of probe molecules to discriminate structural descriptors. We resolve the crystal orbital overlap population (COOP) to specific molecular orbitals and quantify their bonding character, which directly influences vibrational frequencies. Using only a single adsorbate calculation from density function theory (DFT), we compute the interaction energy of individual adsorbate molecular orbitals with adsorption site atomic orbitals across many different sites. Combining the molecular orbital resolved COOP and changes in orbital interaction energy enables probe molecule selection for improved discrimination of various sites. We demonstrate these concepts by comparing the predicted effectiveness of carbon monoxide (CO), nitric oxide (NO), and ethylene (C₂H₄) to probe Pt adsorption sites. Finally, using a previously developed machine learning framework, we show that models trained on hundreds of thousands of C₂H₄ spectra, computed from DFT, which regress surface binding-type and generalized coordination number, outperform those trained using CO and NO spectra. A python package, pDOS_overlap, for implementing the electron density-based analysis on any combination of adsorbates and materials, is also made available.

Infrared spectroscopy data- and physics-driven machine learning for characterizing surface microstructure of complex materials

There is a need to characterize complex materials and their dynamics under reaction conditions to accelerate materials design. Adsorbate vibrational excitations are selective to adsorbate/surface interactions and infrared (IR) spectra associated with activating adsorbate vibrational modes are accurate, capture details of most modes, and can be obtained operando. Current interpretation depends on heuristic peak assignments for simple spectra, precluding the possibility of obtaining detailed structural information. Here, we combine data-based approaches with chemistry-dependent problem formulation to develop physics-driven surrogate models that generate synthetic IR spectra from first-principles calculations. Using synthetic IR spectra of carbon monoxide on platinum, we implement multinomial regression via neural network ensembles to learn probability distributions functions (pdfs) that describe adsorption sites and quantify uncertainty. We use these pdfs to infer detailed surface microstructure from experimental spectra and extend this methodology to other systems as a first step towards characterizing complex interfaces and closing the materials gap.

Stability limits of elemental 2D metals in graphene pores

Two-dimensional (2D) materials can be used as stabilizing templates for exotic nanostructures, including pore-stabilized, free-standing patches of elemental metal monolayers. Although these patches represent metal clusters under extreme conditions and are thus bound for investigations, they are poorly understood as their energetic stability trends and the most promising elements remain unknown. Here, using density-functional theory simulations and the liquid drop model to explore the properties of 45 elemental metal candidates, we identify metals that enable the largest and most stable patches. Simulations show that pores can stabilize patches up to ∼8 nm² areas and that the most prominent candidate in a graphene template is Cu. The results, which are generalizable to templates also beyond graphene, provide encouragement for further, even more resolute experimental pursuit of 2D metals.

Properties of real metallic surfaces: Effects of density functional semilocality and van der Waals nonlocality

We have computed the surface energies, work functions, and interlayer surface relaxations of clean (111), (100), and (110) surfaces of Al, Cu, Ru, Rh, Pd, Ag, Pt, and Au. We interpret the surface energy from liquid metal measurements as the mean of the solid-state surface energies over these three lowest-index crystal faces. We compare experimental (and random phase approximation) reference values to those of a family of nonempirical semilocal density functionals, from the basic local density approximation (LDA) to our most advanced general purpose meta-generalized gradient approximation, strongly constrained and appropriately normed (SCAN). The closest agreement is achieved by the simplest density functional LDA, and by the most sophisticated one, SCAN+rVV10 (Vydrov-Van Voorhis 2010). The long-range van der Waals interaction, incorporated through rVV10, increases the surface energies by about 10%, and increases the work functions by about 3%. LDA works for metal surfaces through two known error cancellations. The Perdew-Burke-Ernzerhof generalized gradient approximation tends to underestimate both surface energies (by about 24%) and work functions (by about 4%), yielding the least-accurate results. The amount by which a functional underestimates these surface properties correlates with the extent to which it neglects van der Waals attraction at intermediate and long range. Qualitative arguments are given for the signs of the van der Waals contributions to the surface energy and work function. A standard expression for the work function in Kohn-Sham (KS) theory is shown to be valid in generalized KS theory. Interlayer relaxations from different functionals are in reasonable agreement with one another, and usually with experiment.

On the relationship between the basicity of a surface and its ability to catalyze transesterification in liquid and gas phases: the case of MgO

The influence of the basic properties of MgO is not the same for liquid and for gas phase transesterification.

Dislocation mechanism of deuterium retention in tungsten under plasma implantation

We have developed a new theoretical model for deuterium (D) retention in tungsten-based alloys on the basis of its being trapped at dislocations and transported to the surface via the dislocation network with parameters determined by ab initio calculations. The model is used to explain experimentally observed trends of D retention under sub-threshold implantation, which does not produce stable lattice defects to act as traps for D in conventional models. Saturation of D retention with implantation dose and effects due to alloying of tungsten with, e.g. tantalum, are evaluated, and comparison of the model predictions with experimental observations under high-flux plasma implantation conditions is presented.

Computational studies of conductivity in wide-band-gap semiconductors and oxides

The ability to control conductivity is essential for design and fabrication of (opto)electronic devices. Such conductivity control has traditionally been very difficult in wide-band-gap semiconductors, and native point defects have often been invoked to explain these problems. State-of-the-art first-principles calculations based on density functional theory have been used to elucidate these issues. Approaches for overcoming the 'band-gap problem', including the LDA+U method, allow more accurate comparisons and predictions of defect levels. The methodology is illustrated with the case of native point defects in zinc oxide. Computations reveal that the prevailing n-type conductivity cannot be attributed to native defects; it must thus be caused by impurities that are unintentionally incorporated. Hydrogen is shown to be an excellent candidate for such an impurity.

Vacancies in metals: from first-principles calculations to experimental data

We have revealed, and resolved, an apparent inability of density functional theory, within the local density and generalized gradient approximations, to describe vacancies in Al accurately and consistently. The shortcoming is due to electron correlation effects near electronic edges and we show how to correct for them. We find that the divacancy in Al is energetically unstable and we show that anharmonic atomic vibrations explain the non-Arrhenius temperature dependence of the vacancy concentration.