Global Optimization of Adsorbate–Surface Structures While Preserving Molecular Identity

The Structure, Oxidation States, and Energetics of Co Nanoparticles on CeO2(111): An STM and DFT Study

Co nanoparticles (NPs) dispersed on ceria have been widely studied as active catalytic materials for many industrially relevant reactions. The detailed nature of such particles and the factors affecting their interaction with ceria remain to be better understood. In this study, a very low coverage (∼0.02 ML) of Co is deposited on a model CeO2(111) thin-film surface and is examined using scanning tunneling microscopy (STM) and X-ray photoelectron spectroscopy (XPS). The Co NPs that nucleate on terrace sites grow with coverage in this range to a maximum size of ca. 40 Co atoms, with an average diameter and height of 16.1 and 1.1 Å, respectively. Global minimization of the structures of Co NPs consisting of up to 23 Co atoms on CeO2(111) is performed based on the minima hopping algorithm and density functional theory (DFT) calculations, and the energetic and chemical properties of the resulting NPs are analyzed. While the theoretical findings are consistent with the STM observations on the strong Co-ceria interactions and the prevalence of oxidic Co species, some notable discrepancies are identified, including inconsistent aspect ratios and the existence of a low oxidation state Coδ+ species. The combined experimental and theoretical findings provide new insights into Co NPs formed on ceria and identify areas requiring further investigation.

Graph Neural Network Model Accelerates Biomass Adsorption Energy Prediction on Iron-group Hydrotalcite Electrocatalysts

Iron-group layered double hydroxides (LDH) have demonstrated excellent biomass electrooxidation performance. However, the development of these materials relies on extensive experiments and high computational costs. Therefore, we developed a graph neural network (GNN) (named GALE-Net 2.0) for predicting the adsorption energies in the electrocatalytic reaction of 5-hydroxymethylfurfural (HMF). A data set of the adsorption energies of organic molecules on the LDH was constructed. The GNN model predicted that the 1:2 CoNi-doped LDH catalyst would demonstrate excellent HMF electrooxidation performance. The calculation time was reduced from 24 h with the density functional theory (DFT) calculations to 1 h with the GALE-Net 2.0. The mean absolute error of the GNN model was 0.17 eV, which is consistent with the accuracy of the DFT calculations. Moreover, the model showed some generality as it successfully predicted the adsorption energy of furan derivatives. Our results suggest that GALE-Net 2.0 can accelerate the design of electrocatalysts.

Rational design of nanoscale stabilized oxide catalysts for OER with OC22

The efficiency of H2 production via water electrolysis is limited by the sluggish oxygen evolution reaction (OER). As such, significant emphasis has been placed upon improving the rate of OER through the anode catalyst. More recently, the Open Catalyst 2022 (OC22) framework has provided a large dataset of density functional theory (DFT) calculations for OER intermediates on the surfaces of oxides. When coupled with state-of-the-art graph neural network models, total energy predictions can be achieved with a mean absolute error as low as 0.22 eV. In this work, we interpolated a database of the total energy predictions for all slabs and OER surface intermediates for 4119 oxide materials in the original OC22 dataset using pre-trained models from the OC22 framework. This database includes all terminations of all facets up to a maximum Miller index of 1. To demonstrate the full utility of this database, we constructed a flexible screening framework to identify viable candidate anode catalysts for OER under varying reaction conditions for bulk, surface, and nanoscale Pourbaix stability as well as material cost, overpotential, and metastability. From our assessment, we were able to identify 122 and 68 viable candidates for OER under the bulk and nanoscale regime, respectively.

Artificial intelligence driven molecule adsorption prediction (AIMAP) applied to chirality recognition of amino acid adsorption on metals

Predicting the adsorption structure of molecules has long been a challenging topic given the coupled complexity of surface binding sites and molecule flexibility. Here, we develop AIMAP, an Artificial Intelligence Driven Molecule Adsorption Prediction tool, to achieve the general-purpose end-to-end prediction of molecule adsorption structures. AIMAP features efficient exploration of the global potential energy surface of the adsorption system based on global neural network (G-NN) potential, by rapidly screening qualified adsorption patterns and fine searching using stochastic surface walking (SSW) global optimization. We demonstrate the AIMAP efficiency in constructing the Cu-HCNO6 adsorption database, encompassing 1 182 351 distinct adsorption configurations of 9592 molecules on three copper surfaces. AIMAP is then utilized to identify the best adsorption structure for 18 amino acids (AAs) on achiral Cu surfaces and the chiral Cu(3,1,17) S surface. We find that AAs chemisorb on copper surfaces in their highest deprotonated state, through both the carboxylate-amino skeleton and side groups. The chiral recognition is identified for the d-preference of Asp, Glu, and Tyr, and l-preference for His. The physical origin for the enantiospecific adsorption is thus rationalized, pointing to the critical role of the competitive adsorption between functional side groups and the carboxylate-amino skeleton at surface low-coordination sites.

Effective nitrogen doping of TiO2 polymorphs at mild temperatures for visible-light-responsive hydrogen evolution

Herein, a mild-temperature nitrogen doping route with the urea-derived gaseous species as the active doping agent is proposed to realize visible-light-responsive photocatalytic hydrogen evolution both for the anatase and rutile TiO2. DFT simulations reveal that the cyanic acid (HOCN), derived from the decomposition of urea, plays a curial role in the effective doping of nitrogen in TiO2 at mild temperatures. Photocatalytic performance demonstrates that both the anatase and rutile TiO2 doped at mild temperatures exhibit the highest hydrogen evolution rates, although the ones prepared at high temperatures possess higher absorbance in the visible range. Steady-state and transient surface photovoltage characterizations of these doped TiO2 polymorphs prepared at different temperatures reveal that harsh conditions (high temperature reaction) typically result in the formation of intrinsic defects that are detrimental to the transport of the low-energy visible-light-induced electrons, while the mild-temperature nitrogen-doping could flatten the pristine upward band bending without triggering the formation of Ti3+, thus achieving enhanced visible-light-responsive hydrogen evolution rates. We anticipate that our findings will provide inspiring information for shrinking the gap between the visible-light-absorbance and the visible-light-responsiveness in the band engineering of wide-bandgap metal-oxide photocatalysts.

GPAW: An open Python package for electronic structure calculations

We review the GPAW open-source Python package for electronic structure calculations. GPAW is based on the projector-augmented wave method and can solve the self-consistent density functional theory (DFT) equations using three different wave-function representations, namely real-space grids, plane waves, and numerical atomic orbitals. The three representations are complementary and mutually independent and can be connected by transformations via the real-space grid. This multi-basis feature renders GPAW highly versatile and unique among similar codes. By virtue of its modular structure, the GPAW code constitutes an ideal platform for the implementation of new features and methodologies. Moreover, it is well integrated with the Atomic Simulation Environment (ASE), providing a flexible and dynamic user interface. In addition to ground-state DFT calculations, GPAW supports many-body GW band structures, optical excitations from the Bethe-Salpeter Equation, variational calculations of excited states in molecules and solids via direct optimization, and real-time propagation of the Kohn-Sham equations within time-dependent DFT. A range of more advanced methods to describe magnetic excitations and non-collinear magnetism in solids are also now available. In addition, GPAW can calculate non-linear optical tensors of solids, charged crystal point defects, and much more. Recently, support for graphics processing unit (GPU) acceleration has been achieved with minor modifications to the GPAW code thanks to the CuPy library. We end the review with an outlook, describing some future plans for GPAW.

Graph Neural Network-Accelerated Multitasking Genetic Algorithm for Optimizing PdxTi1-xHy Surfaces under Various CO2 Reduction Reaction Conditions

Palladium (Pd) hydride-based catalysts have been reported to have excellent performance in the CO2 reduction reaction (CO2RR) and hydrogen evolution reaction (HER). Our previous work on doped PdH and Pd alloy hydrides showed that Ti-doped and Ti-alloyed Pd hydrides could improve the performance of the CO2 reduction reaction compared with pure Pd hydride. Compositions and chemical orderings of the surfaces with only one adsorbate under certain reaction conditions are linked to their stability, activity, and selectivity toward the CO2RR and HER, as shown in our previous work. In fact, various coverages, types, and mixtures of the adsorbates, as well as state variables such as temperature, pressure, applied potential, and chemical potential, could impact their stability, activity, and selectivity. However, these factors are usually fixed at common values to reduce the complexity of the structures and the complexity of the reaction conditions in most theoretical work. To address the complexities above and the huge search space, we apply a deep learning-assisted multitasking genetic algorithm to screen for PdxTi1-xHy surfaces containing multiple adsorbates for CO2RR under different reaction conditions. The ensemble deep learning model can greatly speed up the structure relaxations and retain a high accuracy and low uncertainty of the energy and forces. The multitasking genetic algorithm simultaneously finds globally stable surface structures under each reaction condition. Finally, 23 stable structures are screened out under different reaction conditions. Among these, Pd0.56Ti0.44H1.06 + 25%CO, Pd0.31Ti0.69H1.25 + 50%CO, Pd0.31Ti0.69H1.25 + 25%CO, and Pd0.88Ti0.12H1.06 + 25%CO are found to be very active for CO2RR and suitable to generate syngas consisting of CO and H2.

Stay Hydrated! Impact of Solvation Phenomena on the CO2 Reduction Reaction at Pb(100) and Ag(100) surfaces

Herein, a comprehensive computational study of the impact of solvation on the reduction reaction of CO2 to formic acid (HCOOH) and carbon monoxide on Pb(100) and Ag(100) surfaces is presented. Results further the understanding of how solvation phenomena influence the adsorption energies of reaction intermediates. We applied an explicit solvation scheme harnessing a combined density functional theory (DFT)/microkinetic modeling approach for the CO2 reduction reaction. This approach reveals high selectivities for CO formation at Ag and HCOOH formation on Pb, resolving the prior disparity between ab initio calculations and experimental observations. Furthermore, the detailed analysis of adsorption energies of relevant reaction intermediates shows that the total number of hydrogen bonds formed by HCOO plays a primary role for the adsorption strength of intermediates and the electrocatalytic activity. Results emphasize the importance of explicit solvation for adsorption and electrochemical reaction phenomena on metal surfaces.

Equivariant Graph-Representation-Based Actor-Critic Reinforcement Learning for Nanoparticle Design

We have developed an actor-critic-type policy-based reinforcement learning (RL) method to find low-energy nanoparticle structures and compared its effectiveness to classical basin-hopping. We took a molecule building approach where nanoalloy particles can be regarded as metallic molecules, albeit with much higher flexibility in structure. We explore the strengths of our approach by tasking an agent with the construction of stable mono- and bimetallic clusters. Following physics, an appropriate reward function and an equivariant molecular graph representation framework is used to learn the policy. The agent succeeds in finding well-known stable configuration for small clusters in both single and multicluster experiments. However, for certain use cases the agent lacks generalization to avoid overfitting. We relate this to the pitfalls of actor-critic methods for molecular design and discuss what learning properties an agent will require to achieve universality.

Global optimization of extraframework ensembles in zeolites: structural analysis of extraframework aluminum species in MOR and MFI zeolites

Metal-modified zeolites are versatile catalytic materials with a wide range of industrial applications. Their catalytic behaviour is determined by the nature of externally introduced cationic species, i.e., its geometry, chemical composition, and location within the zeolite pores. Superior catalyst designs can be unlocked by understanding the confinement effect and spatial limitations of the zeolite framework and its influence on the geometry and location of such cationic active sites. In this study, we employ the genetic algorithm (GA) global optimization method to investigate extraframework aluminum species and their structural variations in different zeolite matrices. We focus on extraframework aluminum (EFAl) as a model system because it greatly influences the product selectivity and catalytic stability in several zeolite catalyzed processes. Specifically, the GA was used to investigate the configurational possibilities of EFAl within the mordenite (MOR) and ZSM-5 frameworks. The xTB semi-empirical method within the GA was employed for an automated sampling of the EFAl-zeolite space. Furthermore, geometry refinement at the density functional theory (DFT) level of theory allowed us to improve the most stable configurations obtained from the GA and elaborate on the limitations of the xTB method. A subsequent ab initio thermodynamics analysis (aiTA) was chosen to predict the most favourable EFAl structure(s) under the catalytically relevant operando conditions.

Surface Coverage as an Important Parameter for Predicting Selectivity Trends in Electrochemical CO2 Reduction

The electrochemical CO2 reduction reaction (CO2RR) is important for a sustainable future. Key insights into the reaction pathways have been obtained by density functional theory (DFT) analysis, but so far, DFT has been unable to give an overall understanding of selectivity trends without important caveats. We show that an unconsidered parameter in DFT models of electrocatalysts-the surface coverage of reacting species-is crucial for understanding the CO2RR selectivities for different surfaces. Surface coverage is a parameter that must be assumed in most DFT studies of CO2RR electrocatalysts, but so far, only the coverage of nonreacting adsorbates has been treated. Explicitly treating the surface coverage of reacting adsorbates allows for an investigation that can more closely mimic operating conditions. Furthermore, and of more immediate importance, the use of surface coverage-dependent adsorption energies allows for the extraction of ratios of adsorption energies of CO2RR intermediates (COOHads and HCOOads) that are shown to be predictive of selectivity and are not susceptible to systematic errors. This approach allows for categorization of the selectivity of several monometallic catalysts (Pt, Pd, Au, Ag, Zn, Cu, Rh, W, Pb, Sn, In, Cd, and Tl), even problematic ones such as Ag or Zn, and does so by only considering the adsorption energies of known intermediates. The selectivity of the further reduction of COOHads can now be explained by a preference for Tafel or Heyrovsky reactions, recontextualizing the nature of selectivity of some catalysts. In summary, this work resolves differences between DFT and experimental studies of the CO2RR and underlines the importance of surface coverage.

Modeling Operando Electrochemical CO2 Reduction

Since the seminal works on the application of density functional theory and the computational hydrogen electrode to electrochemical CO₂ reduction (eCO₂R) and hydrogen evolution (HER), the modeling of both reactions has quickly evolved for the last two decades. Formulation of thermodynamic and kinetic linear scaling relationships for key intermediates on crystalline materials have led to the definition of activity volcano plots, overpotential diagrams, and full exploitation of these theoretical outcomes at laboratory scale. However, recent studies hint at the role of morphological changes and short-lived intermediates in ruling the catalytic performance under operating conditions, further raising the bar for the modeling of electrocatalytic systems. Here, we highlight some novel methodological approaches employed to address eCO₂R and HER reactions. Moving from the atomic scale to the bulk electrolyte, we first show how ab initio and machine learning methodologies can partially reproduce surface reconstruction under operation, thus identifying active sites and reaction mechanisms if coupled with microkinetic modeling. Later, we introduce the potential of density functional theory and machine learning to interpret data from Operando spectroelectrochemical techniques, such as Raman spectroscopy and extended X-ray absorption fine structure characterization. Next, we review the role of electrolyte and mass transport effects. Finally, we suggest further challenges for computational modeling in the near future as well as our perspective on the directions to follow.

Automated exploitation of the big configuration space of large adsorbates on transition metals reveals chemistry feasibility

Mechanistic understanding of large molecule conversion and the discovery of suitable heterogeneous catalysts have been lagging due to the combinatorial inventory of intermediates and the inability of humans to enumerate all structures. Here, we introduce an automated framework to predict stable configurations on transition metal surfaces and demonstrate its validity for adsorbates with up to 6 carbon and oxygen atoms on 11 metals, enabling the exploration of ~10⁸ potential configurations. It combines a graph enumeration platform, force field, multi-fidelity DFT calculations, and first-principles trained machine learning. Clusters in the data reveal groups of catalysts stabilizing different structures and expose selective catalysts for showcase transformations, such as the ethylene epoxidation on Ag and Cu and the lack of C-C scission chemistry on Au. Deviations from the commonly assumed atom valency rule of small adsorbates are also manifested. This library can be leveraged to identify catalysts for converting large molecules computationally.

The discovery of heterogeneous catalysts for large molecule conversion has been lagging due to the combinatorial inventory of intermediates. Here, the author presents an automated framework to explore the chemical space of reaction intermediates.

Structural Evolution and Underlying Mechanism of Single-Atom Centers on Mo2C(100) Support during Oxygen Reduction Reaction

The single-metal atoms coordinating with the surface atoms of the support constitute the active centers of as-prepared single-atom catalysts (SACs). However, under hash electrochemical conditions, (1) supports' surfaces may experience structural change, which turn to be distinct from those at ambient conditions; (2) during catalysis, the dynamic responses of a single atom to the attack of reaction intermediates likely change the coordination environment of a single atom. These factors could alter the performance of SACs. Herein, we investigate these issues using Mo₂C(100)-supported single transition-metal (TM) atoms as model SACs toward catalyzing the oxygen reduction reaction (ORR). It is found that the Mo₂C(100) surface is oxidized under ORR turnover conditions, resulting in significantly weakened bonding between single TM atoms and the Mo₂C(100) surface (TM@Mo₂C(100)_O* term for SAC). While the intermediate in 2 e^- ORR does not change the local structures of the active centers in these SACs, the O* intermediate emerging in 4 e^- ORR can damage Rh@ and Cu@Mo₂C(100)_O*. Furthermore, on the basis of these findings, we propose Pt@Mo₂C(100)_O* as a qualified ORR catalyst, which exhibits extraordinary 4 e^- ORR activity with an overpotential of only 0.33 V, surpassing the state-of-the-art Pt(111), and thus being identified as a promising alternative to the commercial Pt/C catalyst.

Chemoselective heterogeneous iridium catalyzed hydrogenation of cinnamalaniline

A selective atmospheric hydrogenation of unsaturated imines over heterogeneous iridium catalyst is described, in addition the selectivity is elucidated by DFT-calculations.

Acetonitrile Transition Metal Interfaces from First Principles

Acetonitrile is among the most commonly used nonaqueous solvents in catalysis and electrochemistry. We study its interfaces with multiple facets of the metals Ag, Cu, Pt, and Rh using density functional theory calculations; the structures reported shed new light on experimental observations and underscore the importance of solvent-solvent interactions at high coverage. We investigate the relationship of potential of zero charge (PZC) to metal work function, reporting results in agreement with experimental measurements. We develop a model to explain the effects of solvent chemisorption and orientation on the PZC to within a mean absolute deviation of 0.08-0.12 V for all facets studied. Our electrostatic field dependent phase diagram agrees with spectroscopic observations and sheds new light on electrostatic field effects. This work provides new insight into experimental observations on acetonitrile metal interfaces and provides guidance for future studies of acetonitrile and other nonaqueous solvent interfaces with transition metals.

Double layer charging driven carbon dioxide adsorption limits the rate of electrochemical carbon dioxide reduction on Gold

Electrochemical CO\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{2}$$\end{document}2 reduction is a potential route to the sustainable production of valuable fuels and chemicals. Here, we perform CO\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{2}$$\end{document}2 reduction experiments on Gold at neutral to acidic pH values to elucidate the long-standing controversy surrounding the rate-limiting step. We find the CO production rate to be invariant with pH on a Standard Hydrogen Electrode scale and conclude that it is limited by the CO\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{2}$$\end{document}2 adsorption step. We present a new multi-scale modeling scheme that integrates ab initio reaction kinetics with mass transport simulations, explicitly considering the charged electric double layer. The model reproduces the experimental CO polarization curve and reveals the rate-limiting step to be *COOH to *CO at low overpotentials, CO\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{2}$$\end{document}2 adsorption at intermediate ones, and CO\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{2}$$\end{document}2 mass transport at high overpotentials. Finally, we show the Tafel slope to arise from the electrostatic interaction between the dipole of *CO\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{2}$$\end{document}2 and the interfacial field. This work highlights the importance of surface charging for electrochemical kinetics and mass transport.

Electrochemical CO₂ reduction is a potential route to the sustainable production of valuable fuels and chemicals. In this joint experimental-theoretical work, the authors address the issue of the rate-limiting step on Gold and present insights from multi-scale simulations into the importance of the electric double layer on reaction kinetics and mass transport.

Surface Pyroelectricity in Cubic SrTiO3

Symmetry-imposed restrictions on the number of available pyroelectric and piezoelectric materials remain a major limitation as 22 out of 32 crystallographic material classes exhibit neither pyroelectricity nor piezoelectricity. Yet, by breaking the lattice symmetry it is possible to circumvent this limitation. Here, using a unique technique for measuring transient currents upon rapid heating, direct experimental evidence is provided that despite the fact that bulk SrTiO₃ is not pyroelectric, the (100) surface of TiO₂ -terminated SrTiO₃ is intrinsically pyroelectric at room temperature. The pyroelectric layer is found to be ≈1 nm thick and, surprisingly, its polarization is comparable with that of strongly polar materials such as BaTiO₃ . The pyroelectric effect can be tuned ON/OFF by the formation or removal of a nanometric SiO₂ layer. Using density functional theory, the pyroelectricity is found to be a result of polar surface relaxation, which can be suppressed by varying the lattice symmetry breaking using a SiO₂ capping layer. The observation of pyroelectricity emerging at the SrTiO₃ surface also implies that it is intrinsically piezoelectric. These findings may pave the way for observing and tailoring piezo- and pyroelectricity in any material through appropriate breaking of symmetry at surfaces and artificial nanostructures such as heterointerfaces and superlattices.

Noble metal supported hexagonal boron nitride for the oxygen reduction reaction: a DFT study

Discovering active, stable and cost-effective catalysts for the oxygen reduction reaction (ORR) is of utmost interest for commercialization of fuel cells. Scarce and expensive noble metals such as Pt and Pd are the state-of-the-art active ORR catalysts but suffer from low stability against CO poisoning. Hexagonal boron nitride (h-BN) is a particularly attractive material due to its low cost and stability; however, it suffers from intrinsic low activity toward the ORR in the pristine form as a result of its inherently low conductivity with a large band gap of ∼5.5 electron volts. During the past few years, several strategies such as using metal supports, metal doping and atomic vacancies have been reported to significantly increase the conductivity, thereby promoting the ORR activity. Herein we use density functional theory calculations to systematically study these strategies for activating inert h-BN and further examine the stability against CO poisoning. We show that noble metals, such as Ag, Pd, and Pt, require boron (B) or nitrogen (N) vacancies to reasonably activate h-BN toward the ORR. For example, Pd supported h-BN with B-vacancies exhibits significantly high ORR activity. All three examined metal supported h-BNs are predicted to be stable against CO poisoning. These results demonstrate that supporting h-BN on noble metals is a promising strategy to increase the stability against CO poisoning while maintaining high ORR activity.

Rapid estimation of activation energy in heterogeneous catalytic reactions via machine learning

Estimation of activation energies within heterogeneous catalytic reactions is performed using machine learning and catalysts dataset. In particular, descriptors for determining activation energy are revealed within the 788 activation energy dataset. With the implementation of machine learning and chosen descriptors, activation energy can be instantly predicted with over 90% accuracy during cross-validation. Thus, rapid estimation of activation energies within heterogeneous catalytic reactions can be made achievable via machine learning, leading toward the acceleration of catalysts design and characterization. © 2018 Wiley Periodicals, Inc.

Tracking Metal Electrodeposition Dynamics from Nucleation and Growth of a Single Atom to a Crystalline Nanoparticle

In electrodeposition the key challenge is to obtain better control over nanostructure morphology. Currently, a lack of understanding exists concerning the initial stages of nucleation and growth, which ultimately impact the physicochemical properties of the resulting entities. Using identical location scanning transmission electron microscopy (STEM), with boron-doped diamond (BDD) serving as both an electron-transparent TEM substrate and electrode, we follow this process, from the formation of an individual metal atom through to a crystalline metal nanoparticle, under potential pulsed conditions. In doing so, we reveal the importance of electrochemically driven atom transport, atom cluster formation, cluster progression to a nanoparticle, and the mechanism by which neighboring particles interact during growth. Such information will help formulate improved nucleation and growth models and promote wider uptake of electrodeposited structures in a wide range of societally important applications. This type of measurement is possible in the TEM because the BDD possesses inherent stability, has an extremely high thermal conductivity, is electron beam transparent, is free from contamination, and is robust enough for multiple deposition and imaging cycles. Moreover, the platform can be operated under conditions such that we have confidence that the dynamic atom events we image are truly due to electrochemically driven deposition and no other factors, such as electron-beam-induced movement.

DFT investigation on the adsorption of munition compounds on α-Fe2O3: similarity and differences with α-Al2O3

Arid environments have long been a testing and training ground for novel munitions. However, these activities leave behind unknown quantities of munition residues with unknown impact on local flora and fauna. In particular, arid soil contains Lewis acidic metal oxides which bind and catalyze the electron rich substituent groups commonly found in munition compounds, although the exact mechanisms are poorly understood. The current study remedies this lack of knowledge by utilizing density functional theory (DFT) to explore various orientations of four important munition compounds on the α-Fe2O3(0001) and α-Al2O3(0001) surfaces. Our findings reveal that while α-Fe2O3 binds the munition compounds more strongly than α-Al2O3, all four compounds experienced elongation of their nitro (-NO2) groups, indicating their susceptibility towards degradation on these surfaces.

Theoretical Investigations of the Electrochemical Reduction of CO on Single Metal Atoms Embedded in Graphene

Single transition metal atoms embedded at single vacancies of graphene provide a unique paradigm for catalytic reactions. We present a density functional theory study of such systems for the electrochemical reduction of CO. Theoretical investigations of CO electrochemical reduction are particularly challenging in that electrochemical activation energies are a necessary descriptor of activity. We determined the electrochemical barriers for key proton-electron transfer steps using a state-of-the-art, fully explicit solvent model of the electrochemical interface. The accuracy of GGA-level functionals in describing these systems was also benchmarked against hybrid methods. We find the first proton transfer to form CHO from CO to be a critical step in C₁ product formation. On these single atom sites, the corresponding barrier scales more favorably with the CO binding energy than for 211 and 111 transition metal surfaces, in the direction of improved activity. Intermediates and transition states for the hydrogen evolution reaction were found to be less stable than those on transition metals, suggesting a higher selectivity for CO reduction. We present a rate volcano for the production of methane from CO. We identify promising candidates with high activity, stability, and selectivity for the reduction of CO. This work highlights the potential of these systems as improved electrocatalysts over pure transition metals for CO reduction.

Theoretical Understanding of Mechanisms of Proton Exchange Membranes Made of 2D Crystals with Ultrahigh Selectivity

Recent reports on proton conduction across pristine graphene and hexagonal boron nitride (h-BN) provide a new avenue for the design of proton exchange membranes. The uniform pores formed by the electron clouds of two-dimensional (2D) crystals can effectively block the undesired transportation of other species thus ultrahigh selectivity can be achieved. With the aid of first-principles calculations, we investigate the proton conduction process across six kinds of intact 2D crystals, namely graphene, h-BN, β₁₂ boron sheet, χ₃ boron sheet, phosphorene, and silicene. To clarify the proton conduction mechanism, three proton penetration modes are proposed: dissociation-penetration, adsorption-penetration, and direct penetration. Based on our calculation results, for graphene and h-BN without atomic defects, they are unlikely to provide sufficient proton conductivity at room temperature when no bias potential is applied. By contrast, the β₁₂ boron sheet, χ₃ boron sheets, and silicene exhibit relatively lower proton penetration energy barriers, making them prospective candidates for future proton exchange membrane applications.

The atomic simulation environment-a Python library for working with atoms

The atomic simulation environment (ASE) is a software package written in the Python programming language with the aim of setting up, steering, and analyzing atomistic simulations. In ASE, tasks are fully scripted in Python. The powerful syntax of Python combined with the NumPy array library make it possible to perform very complex simulation tasks. For example, a sequence of calculations may be performed with the use of a simple 'for-loop' construction. Calculations of energy, forces, stresses and other quantities are performed through interfaces to many external electronic structure codes or force fields using a uniform interface. On top of this calculator interface, ASE provides modules for performing many standard simulation tasks such as structure optimization, molecular dynamics, handling of constraints and performing nudged elastic band calculations.

Global structure search for molecules on surfaces: Efficient sampling with curvilinear coordinates

Efficient structure search is a major challenge in computational materials science. We present a modification of the basin hopping global geometry optimization approach that uses a curvilinear coordinate system to describe global trial moves. This approach has recently been shown to be efficient in structure determination of clusters [C. Panosetti et al., Nano Lett. 15, 8044-8048 (2015)] and is here extended for its application to covalent, complex molecules and large adsorbates on surfaces. The employed automatically constructed delocalized internal coordinates are similar to molecular vibrations, which enhances the generation of chemically meaningful trial structures. By introducing flexible constraints and local translation and rotation of independent geometrical subunits, we enable the use of this method for molecules adsorbed on surfaces and interfaces. For two test systems, trans-β-ionylideneacetic acid adsorbed on a Au(111) surface and methane adsorbed on a Ag(111) surface, we obtain superior performance of the method compared to standard optimization moves based on Cartesian coordinates.

Hydrocarbon adsorption in an aqueous environment: A computational study of alkyls on Cu(111)

Hydrocarbon chains are important intermediates in various aqueous-phase surface processes, such as CO2 electroreduction, aqueous Fischer-Tropsch synthesis, and aqueous phase reforming of biomass-derived molecules. Further, the interaction between water and adsorbed hydrocarbons represents a difficult case for modern computational methods. Here, we explore various methods for calculating the energetics of this interaction within the framework of density functional theory and explore trade-offs between the use of low water coverages, molecular dynamics approaches, and minima hopping for identification of low energy structures. An effective methodology for simulating low temperature processes is provided by using a unit cell in which the vacuum space is filled with water, employing the minima hopping algorithm to search for low-lying minima, and including dispersion (van der Waals) interactions. Using this methodology, we show that a high coverage of adsorbed alkyls is destabilized by the presence of water, while a low coverage of alkyls is stabilized. Solvation has a small effect on the energetics of hydrocarbon chain growth, generally decreasing its favorability at low temperatures. We studied higher temperatures by running molecular dynamics simulations starting at the minima found by the minima hopping algorithm and found that increased temperatures facilitate chain growth. The self-consistent continuum solvation method effectively describes the alkyl-water interaction and is in general agreement with the explicit solvation results in most cases, but care should be taken at high alkyl coverage.

Oxygen-induced changes to selectivity-determining steps in electrocatalytic CO2 reduction

The state of the electrocatalyst surface—including the oxidation state of the catalyst and the presence of spectator species—is investigated on Cu surfaces with density functional theory in order to understand predicted ramifications on the selectivity of CO₂ reduction between CH₄ and CH₃OH.