A DFT-based genetic algorithm search for AuCu nanoalloy electrocatalysts for CO₂ reduction

Accelerated Structural Optimization for the Supported Metal System Based on Hybrid Approach Combining Bayesian Optimization with Local Search

Numerous systematic methods have been developed to search for the global minimum of the potential energy surface, which corresponds to the optimal atomic structure. However, the majority of them still demand a substantial computing load due to the relaxation process that is embedded as an inner step inside the algorithm. Here, we propose a hybrid approach that combines Bayesian optimization (BO) and a local search that circumvents the relaxation step and efficiently finds the optimum structure, particularly in supported metal systems. The hybridization strategy combining the capabilities of BO's effective exploration and the local search's fast convergence expedites structural search. In addition, the formulation of physical constraints regarding the materials system and the feature of screening structure similarity enhance the computational efficiency of the proposed method. The proposed algorithm is demonstrated in two supported metal systems, showing the potential of the proposed method in the field of structural optimization.

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.

Underlying mechanisms of gold nanoalloys stabilization

Gold nanoclusters have attracted significant attention due to their unique physical-chemical properties, which can be tuned by alloying with elements such as Cu, Pd, Ag, and Pt to design materials for various applications. Although Au-nanoalloys have promising applications, our atomistic understanding of the descriptors that drive their stability is far from satisfactory. To address this problem, we considered 55-atom model nanoalloys that have been synthesized by experimental techniques. Here, we combined data mining techniques for creating a large sample of representative configurations, density functional theory for performing total energy optimizations, and Spearman correlation analyses to identify the most important descriptors. Among our results, we have identified trends in core-shell formation in the AuCu and AuPd systems and an onion-like design in the AuAg system, characterized by the aggregation of gold atoms on nanocluster surfaces. These features are explained by Au's surface energy, packing efficiency, and charge transfer mechanisms, which are enhanced by the alloys' preference for adopting the structure of the alloying metal rather than the low-symmetry one presented by Au55. These generalizations provide insights into the interplay between electronic and structural properties in gold nanoalloys, contributing to the understanding of their stabilization mechanisms and potential applications in various fields.

Growth pathways of Cu shells on Au and AuCu seeds: interdiffusion, shape transformations, strained shells and patchy surfaces

The growth of AuCu nanoparticles obtained by depositing Cu atoms on starting seeds of pure Au and on mixed AuCu seeds is studied by molecular dynamics simulations. Depending on the shape of the seed, its composition and the growth temperature, different growth pathways are observed, in which several types of structural transformations take place. The final growth structures comprise Au@Cu core@shell arrangements as well as Janus-like structures with patchy surfaces. The results of the growth simulations are rationalized in terms of the activation of different diffusion processes, both on the surface and inside the growing clusters. These diffusion processes regulate structural transitions between different motifs and the occurrence of dewetting phenomena. The simulation results show that depositon of Cu atoms on pure Au or mixed AuCu seed can be an effective tool for producing clusters with uncommon surface atom arrangements of potential interest for catalysis.

Ultralow reaction barriers for CO oxidation in Cu-Au nanoclusters

Systematic structure prediction of CunAum nanoclusters was carried out for a wide compositional area (n + m ≤ 15) using the evolutionary algorithm USPEX and DFT calculations. The obtained structural data allowed us to assess the local stability of clusters and their suitability for catalysis of CO oxidation. Using these two criteria, we selected several most promising clusters for an accurate study of their catalytic properties. The adsorption energies of reagents, reaction paths, and activation energies were calculated. We found several cases with low activation energies and explained these cases using the patterns of structural change at the moment of CO2 desorption. The unique case is the Cu7Au6 cluster, which has extremely low activation energies for all transition states (below 0.05 eV). We thus showed that higher flexibility due to the binary nature of nanoclusters makes it possible to achieve the maximum catalytic activity. Considering the lower price of copper, Cu-Au nanoparticles are a promising new family of catalysts.

Nanoporous Intermetallic SnTe Enables Efficient Electrochemical CO2 Reduction into Formate via Promoting the Fracture of Metal-Oxygen Bonding

Electrochemical reduction of CO₂ into formate product is considered the most practical significance link in the carbon cycle. Developing cheap and efficient electrocatalysts with high selectivity for formate on a wide operated potential window is desirable yet challenging. Herein, nanoporous ordered intermetallic tin-tellurium (SnTe) is synthesized with a greater reduction performance for electrochemical CO₂ to formate reduction compared to bare Sn. This nanoporous SnTe achieves 93% Faradaic efficiency for formate production and maintains over 90% Faradaic efficiency at a wide voltage range from -1.0 to -1.3 V versus reversible hydrogen electrode (RHE), together with 60 h stability. Combining operando Raman spectroscopy studies with density functional theory calculations reveals that strong orbital interaction between Sn and neighboring tellurium (Te) in the intermetallic SnTe can lower the barriers of the oxygen cutoff hydrogenation and desorption steps by promoting the fracture of bond between metal and oxygen, leading to the significant enhancement of formate production.

HKUST-1-derived highly ordered Cu nanosheets with enriched edge sites, stepped (211) surfaces and (200) facets for effective electrochemical CO2 reduction

A novel electrode composed of Cu nanosheets constructed from nanoparticles was synthesized by in situ electrochemical derivation from the metal-organic framework (MOF) HKUST-1. The prepared derivative electrode (HE-Cu) exhibited higher Faradaic efficiency (FE, 56.0%) of electrochemical CO₂ reduction (CO₂R) compared with that of pristine Cu foil (p-Cu, 32.3%) at an overpotential of -1.03 V vs. a reversible hydrogen electrode (RHE). HE-Cu also exhibited lower onset potential of CO₂R as well as inhibiting the H₂ evolution reaction. Electrochemical measurements revealed that HE-Cu exhibited higher CO₂ adsorption (1.58-fold) and a larger electrochemical active surface area (1.24-fold) compared with p-Cu. Physicochemical characterization and Tafel analysis showed that stepped Cu (211) surfaces, (200) facets and Cu edge atoms on HE-Cu contributed significantly to the enhanced CO₂R activity and/or HCOOH and/or C2 product selectivity. The FEs of HCOOH and C2 products for HE-Cu increased 1.57-fold and 10.6-fold at an overpotential of -1.19 V vs. RHE compared with p-Cu. Although CH₄ was produced on p-Cu, its formation was totally suppressed on HE-Cu due to the increase of edge sites and (200) facets. Our study demonstrates that electroreduction of MOFs is a promising method to prepare novel and stable electrochemical catalysts with unique surface structures. The fabricated derivative electrode not only promoted electrochemical CO₂R activity but also exhibited high C2 product selectivity.

Accelerated crystal structure prediction of multi-elements random alloy using expandable features

Properties of solid-state materials depend on their crystal structures. In solid solution high entropy alloy (HEA), its mechanical properties such as strength and ductility depend on its phase. Therefore, the crystal structure prediction should be preceded to find new functional materials. Recently, the machine learning-based approach has been successfully applied to the prediction of structural phases. However, since about 80% of the data set is used as a training set in machine learning, it is well known that it requires vast cost for preparing a dataset of multi-element alloy as training. In this work, we develop an efficient approach to predicting the multi-element alloys' structural phases without preparing a large scale of the training dataset. We demonstrate that our method trained from binary alloy dataset can be applied to the multi-element alloys' crystal structure prediction by designing a transformation module from raw features to expandable form. Surprisingly, without involving the multi-element alloys in the training process, we obtain an accuracy, 80.56% for the phase of the multi-element alloy and 84.20% accuracy for the phase of HEA. It is comparable with the previous machine learning results. Besides, our approach saves at least three orders of magnitude computational cost for HEA by employing expandable features. We suggest that this accelerated approach can be applied to predicting various structural properties of multi-elements alloys that do not exist in the current structural database.

Finite-Temperature Structures of Supported Subnanometer Catalysts Inferred via Statistical Learning and Genetic Algorithm-Based Optimization

Single-atom catalysts (SACs) minimize noble metal utilization and can alter the activity and selectivity of supported metal nanoparticles. However, the morphology of active centers, including single atoms and subnanometer clusters of a few atoms, remains elusive due to experimental challenges. The computational cost to describe numerous cluster shapes and sizes makes direct first-principles calculations impractical. We present a computational framework to enable structure determination for single-atom and subnanometer cluster catalysts. As a case study, we obtained the low-energy structures of Pd_n (n = 1-21) clusters supported on CeO₂(111), which are critical components of automobile three-way catalysts. Trained on density functional theory data, a three-dimensional cluster expansion is established using statistical learning to describe the Hamiltonian and predict energies of supported Pd_n clusters of any structure. Low-energy stable and metastable structures are identified using a Metropolis Monte Carlo-based genetic algorithm in the canonical ensemble at 300 K. We observe that supported single atoms sinter to form bilayer clusters, and large cluster isomers share similarities in both shape and energy. The findings elucidate the significance of the support and microstructure on cluster stability. We discovered a simple surrogate structure-energy model, where the energy per atom scales with the square root of the average first coordination number, which can be used to estimate energies and compare the stability of clusters. Our framework, applicable to any metal/support system, fills an important methodological gap to predict the stability of supported metal catalysts in the subnanometer regime.

CLEASE: a versatile and user-friendly implementation of cluster expansion method

Materials exhibiting a substitutional disorder such as multicomponent alloys and mixed metal oxides/oxyfluorides are of great importance in many scientific and technological sectors. Disordered materials constitute an overwhelmingly large configurational space, which makes it practically impossible to be explored manually using first-principles calculations such as density functional theory due to the high computational costs. Consequently, the use of methods such as cluster expansion (CE) is vital in enhancing our understanding of the disordered materials. CE dramatically reduces the computational cost by mapping the first-principles calculation results on to a Hamiltonian which is much faster to evaluate. In this work, we present our implementation of the CE method, which is integrated as a part of the atomic simulation environment (ASE) open-source package. The versatile and user-friendly code automates the complex set up and construction procedure of CE while giving the users the flexibility to tweak the settings and to import their own structures and previous calculation results. Recent advancements such as regularization techniques from machine learning are implemented in the developed code. The code allows the users to construct CE on any bulk lattice structure, which makes it useful for a wide range of applications involving complex materials. We demonstrate the capabilities of our implementation by analyzing the two example materials with varying complexities: a binary metal alloy and a disordered lithium chromium oxyfluoride.

Alloy Nanocatalysts for the Electrochemical Oxygen Reduction (ORR) and the Direct Electrochemical Carbon Dioxide Reduction Reaction (CO2 RR)

In the face of the global energy challenge and progressing global climate change, renewable energy systems and components, such as fuel cells and electrolyzers, which close the energetic oxygen and carbon cycles, have become a technology development priority. The electrochemical oxygen reduction reaction (ORR) and the direct electrochemical carbon dioxide reduction reaction (CO₂ RR) are important electrocatalytic processes that proceed at gas diffusion electrodes of hydrogen fuel cells and CO₂ electrolyzers, respectively. However, their low catalytic activity (voltage efficiency), limited long-term stability, and moderate product selectivity (related to their Faradaic efficiency) have remained challenges. To address these, suitable catalysts are required. This review addresses the current state of research on Pt-based and Cu-based nanoalloy electrocatalysts for ORR and CO₂ RR, respectively, and critically compares and contrasts key performance parameters such as activity, selectivity, and durability. In particular, Pt nanoparticles alloyed with transition metals, post-transition metals and lanthanides, are discussed, as well as the material characterization and their performance for the ORR. Then, bimetallic Cu nanoalloy catalysts are reviewed and organized according to their main reaction product generated by the second metal. This review concludes with a perspective on nanoalloy catalysts for the ORR and the CO₂ RR, and proposes future research directions.

Theoretical study of the structures of bimetallic Ag-Au and Cu-Au clusters up to 108 atoms

The stable structures of Ag-Au and Cu-Au clusters with 1 : 1, 1 : 3 and 3 : 1 compositions with up to 108 atoms are obtained using a modified adaptive immune optimization algorithm with Gupta potential. The dominant motifs of Ag-Au and Cu-Au clusters are decahedron and icosahedron, respectively. However, in Ag-rich Ag-Au clusters, more icosahedra are found, and in Cu-rich Cu-Au clusters, there exist several decahedral motifs. Four Leary tetrahedral motifs are predicted. Cu_coreAu_shell configurations are predicted in Cu-Au clusters. In Ag-Au clusters, most Ag atoms are on the surface, but partial ones are located in the inner shell, while Au atoms are interconnected in the middle shell.

CeO2(111) electronic reducibility tuned by ultra-small supported bimetallic Pt-Cu clusters

Controlling Ce4+ to Ce3+ electronic reducibility in a rare-earth binary oxide such as CeO2 has enormous applications in heterogeneous catalysis, where a profound understanding of reactivity and selectivity at the atomic level is yet to be reached. Thus, in this work we report an extensive DFT-based Basin Hopping global optimization study to find the most stable bimetallic Pt-Cu clusters supported on the CeO2(111) oxide surface, involving up to 5 atoms in size for all compositions. Our PBE+U global optimization calculations indicate a preference for Pt-Cu clusters to adopt 2D planar geometries parallel to the oxide surface, due to the formation of strong metal bonds to oxygen surface sites and charge transfer effects. The calculated adsorption energy values (Eads) for both mono- and bimetallic systems are of the order of 1.79 up to 4.07 eV, implying a strong metal cluster interaction with the oxide surface. Our calculations indicate that at such sub-nanometer sizes, the number of Ce4+ surface atoms reduced to Ce3+ cations is mediated by the amount of Cu atoms within the cluster, reaching a maximum of three Ce3+ for a supported Cu5 cluster. Our computational results have critical implications on the continuous understanding of the strong metal-support interactions over reducible oxides such as CeO2, as well as the advancement of frontier research areas such as heterogeneous single-atom catalysts (SAC) and single-cluster catalysts (SCC).

Physico-Chemical Insights into Gas-Phase and Oxide-Supported Sub-Nanometre AuCu Clusters

Catalysis by AuCu nanoclusters is a promising scientific field. However, our fundamental understanding of the underlying mechanisms of mixing in AuCu clusters at the sub-nanometre scale and their physico-chemical properties in both the gas-phase and on oxide supports is limited. We have identified the global minima of gas-phase and MgO(100)-supported AuCu clusters with 3–10 atoms using the Mexican Enhanced Genetic Algorithm coupled with density functional theory. Au and Cu adatoms and supported dimers have been also simulated at the same level of theory. The most stable composition, as calculated from mixing and binding energies, is obtained when the Cu proportion is close to 50%. The structures of the most stable free AuCu clusters exhibit Cu-core/Au-shell segregation. On the MgO surface however, there is a preference for Cu atoms to lie at the cluster-substrate interface. Due to the interplay between the number of interfacial Cu atoms and surface-induced cluster rearrangement, on the MgO surface 3D structures become more stable than 2D structures. The O-site of MgO surface is found to be the most favourable adsorption site for both metals. All dimers favour vertical (V) configurations on the surface and their adsorption energies are in the order: AuCu < CuCu < AuAu < AuCu (where the underlined atom is bound to the O-site). For both adatoms and AuCu dimers, adsorption via Cu is more favourable than Au-adsorbed configurations, but, this disagrees with the ordering for the pure dimers due to a combination of electron transfer and the metal-on-top effect. Binding energy (and second difference) and HOMO-LUMO gap calculations show that even-atom (even-electron) clusters are more stable than the neighbouring odd-atom (odd- electron) clusters, which is expected for closed- and open-shell systems. Supporting AuCu clusters on the MgO(100) surface decreases the charge transfer between Au and Cu atoms calculated in free clusters. The results of this study may serve as a foundation for designing better AuCu catalysts.

Plug-and-Play Continuous Gas Flow Assembly of Cysteine-Inserted AuCu Nanobimetals for Folate-Receptor-Targeted Chemo-Phototherapy

Conjugatable nanobimetals exhibiting broadband light absorption for use as phototherapeutic platforms were assembled via a plug-and-play continuous gas flow route. Electrically produced AuCu nanobunches (NBs) under nitrogen gas flow were directly injected into cysteine (cys) solution through gas pressurization to mechanically spray the solution (AuCu into cys droplets). The sprayed droplets were then exposed to 185 nm UV light (higher photon energy [6.2 eV] than the work functions of Au [5.1 eV] and Cu [4.7 eV]) to initiate photoionization of AuCu NBs for subsequent electrostatic reaction with the SH^- group of cys to form cys-inserted AuCu (AuCu-cys) platforms in a single-pass gas stream. These platforms exhibited broadband light absorption spectra because of hybridized interparticle plasmonic coupling and could be conjugated to folic acid (FA) when dispersed in FA solution to form highly dispersible, biocompatible, and cancer-targetable AuCu-cys-FA. This material was suitable for use in targeted phototherapy of folate-receptor (FR)-rich cancers via FR-mediated endocytosis, and loading doxorubicin (DOX) into AuCu-cys-FA (i.e., AuCu-cys-DOXFA) facilitated chemo-phototherapy because of photoresponsive anticancer drug release upon induction of hyperthermia.

Structural, electronic and magnetic properties of stoichiometric cobalt oxide clusters (CoO)nq (n=3−10,q=0,+1): A modified basin-hopping Monte Carlo algorithm with spin-polarized DFT

The structural, electronic and magnetic properties of the neutral and cationic cobalt oxide clusters (CoO)[Formula: see text] ([Formula: see text], [Formula: see text]) have been studied using a modified basin-hopping Monte Carlo (BHMC) algorithm refined by spin-polarized DFT. A systematic search of global minimum structures predicts new global minima of (CoO)[Formula: see text] and reproduced other minima that are in excellent agreement with previous works. For most low-spin and high-spin states, the structural transition from planar-like to compact structure occurs at (CoO)[Formula: see text], which is in contrast with the general notion that the structural changes at (CoO)[Formula: see text]. Supported by the results of the binding energy, second-order total energy difference, chemical hardness, chemical potential and HOMO-LUMO gap confirms the stability of (CoO)4. Results of the spin magnetic moments for the global minima show that (CoO)4 and (CoO)8 spin configurations exhibit a fully antiferromagnetic (AFM) ordering, while (CoO)9 spin displays the highest ferromagnetic (FM) ordering. Interestingly, elongation of Co–Co bond in (CoO)4 causes O being polarized by the neighboring Co atoms that accordingly follows the Goodenough-Kanamori-Anderson rule of FM super-exchange coupling for the Co-O-Co structural rearrangement to 90∘ ([Formula: see text] structure) in order to accommodate the spin magnetic ordering changes. This rearrangement is a result of the valence band being shifted away from the Fermi level to lower energy causing high population of the spin-up density of state and leading to the asymmetrical polarization of the whole (CoO)4 structure. As far as the dissociation energy surfaces are concerned, the first ever such surfaces are constructed corresponding to [Formula: see text], which identify a complete dissociation pathway linking the cationic and neutral clusters and finally confirm (CoO)[Formula: see text] as the most stable cluster compared to the rest.

Rich Ground-State Chemical Ordering in Nanoparticles: Exact Solution of a Model for Ag-Au Clusters

We show that nanoparticles can have very rich ground-state chemical order. This is illustrated by determining the chemical ordering of Ag-Au 309-atom Mackay icosahedral nanoparticles. The energy of the nanoparticles is described using a cluster expansion model, and a mixed integer programming approach is used to find the exact ground-state configurations for all stoichiometries. The chemical ordering varies widely between the different stoichiometries and displays a rich zoo of structures with nontrivial ordering.

Active learning with non-ab initio input features toward efficient CO2 reduction catalysts

In this work, we propose the use of the d-band width of the muffin-tin orbital theory (to account for the local coordination environment) plus electronegativity (to account for adsorbate renormalization) as a simple set of alternative descriptors for chemisorption which do not require ab initio calculations for large-scale first-hand screening.

In a conventional chemisorption model, the d-band center theory (augmented sometimes with the upper edge of the d-band for improved accuracy) plays a central role in predicting adsorption energies and catalytic activity as a function of the d-band center of solid surfaces, but it requires density functional calculations that can be quite costly for the purposes of large scale screening of materials. In this work, we propose to use the d-band width of the muffin-tin orbital theory (to account for the local coordination environment) plus electronegativity (to account for adsorbate renormalization) as a simple set of alternative descriptors for chemisorption which do not require ab initio calculations for large-scale first-hand screening. This pair of descriptors is then combined with machine learning methods, namely, neural network (NN) and kernel ridge regression (KRR). We show, for a toy set of 263 alloy systems, that the CO adsorption energy on the (100) facet can be predicted with a mean absolute deviation error of 0.05 eV. We achieved this high accuracy by utilizing an active learning algorithm, without which the accuracy was 0.18 eV. In addition, the results of testing the method with other facets such as (111) terrace and (211) step sites suggest that the present model is also capable of handling different coordination environments effectively. As an example of the practical application of this machine, we identified Cu₃Y@Cu* as an active and cost-effective electrochemical CO₂ reduction catalyst to produce CO with an overpotential ∼1 V lower than a Au catalyst.

Metastable Structures in Cluster Catalysis from First-Principles: Structural Ensemble in Reaction Conditions and Metastability Triggered Reactivity

Reactivity studies on catalytic transition metal clusters are usually performed on a single global minimum structure. With the example of a Pt₁₃ cluster under a pressure of hydrogen, we show from first-principle calculations that low energy metastable structures of the cluster can play a major role for catalytic reactivity and that hence consideration of the global minimum structure alone can severely underestimate the activity. The catalyst is fluxional with an ensemble of metastable structures energetically accessible at reaction conditions. A modified genetic algorithm is proposed to comprehensively search for the low energy metastable ensemble (LEME) structures instead of merely the global minimum structure. In order to reduce the computational cost of density functional calculations, a high dimensional neural network potential is employed to accelerate the exploration. The presence and influence of LEME structures during catalysis is discussed by the example of H covered Pt₁₃ clusters for two reactions of major importance: hydrogen evolution reaction and methane activation. The results demonstrate that although the number of accessible metastable structures is reduced under reaction condition for Pt₁₃ clusters, these metastable structures can exhibit high activity and dominate the observed activity due to their unique electronic or structural properties. This underlines the necessity of thoroughly exploring the LEME structures in catalysis simulations. The approach enables one to systematically address the impact of isomers in catalysis studies, taking into account the high adsorbate coverage induced by reaction conditions.

Predicted Structures of the Active Sites Responsible for the Improved Reduction of Carbon Dioxide by Gold Nanoparticles

Gold (Au) nanoparticles (NPs) are known experimentally to reduce carbon dioxide (CO₂) to carbon monoxide (CO), with far superior performance to Au foils. To obtain guidance in designing improved CO₂ catalysts, we want to understand the nature of the active sites on Au NPs. Here, we employed multiscale atomistic simulations to computationally synthesize and characterize a 10 nm thick Au NP on a carbon nanotube (CNT) support, and then we located active sites from quantum mechanics (QM) calculations on 269 randomly selected sites. The standard scaling relation is that the formation energy of *COOH (ΔE_*COOH) is proportional to the binding energy of *CO (E^binding_*CO); therefore, decreasing ΔE_*COOH to boost the CO₂ reduction reaction (CO₂RR) causes an increase of E^binding_*CO that retards CO₂RR. We show that the NPs have superior CO₂RR because there are many sites at the twin boundaries that significantly break this scaling relation.

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.

Recent Advances in Inorganic Heterogeneous Electrocatalysts for Reduction of Carbon Dioxide

In view of the climate changes caused by the continuously rising levels of atmospheric CO2 , advanced technologies associated with CO2 conversion are highly desirable. In recent decades, electrochemical reduction of CO2 has been extensively studied since it can reduce CO2 to value-added chemicals and fuels. Considering the sluggish reaction kinetics of the CO2 molecule, efficient and robust electrocatalysts are required to promote this conversion reaction. Here, recent progress and opportunities in inorganic heterogeneous electrocatalysts for CO2 reduction are discussed, from the viewpoint of both experimental and computational aspects. Based on elemental composition, the inorganic catalysts presented here are classified into four groups: metals, transition-metal oxides, transition-metal chalcogenides, and carbon-based materials. However, despite encouraging accomplishments made in this area, substantial advances in CO2 electrolysis are still needed to meet the criteria for practical applications. Therefore, in the last part, several promising strategies, including surface engineering, chemical modification, nanostructured catalysts, and composite materials, are proposed to facilitate the future development of CO2 electroreduction.

Decoupling strain and ligand effects in ternary nanoparticles for improved ORR electrocatalysis

Ternary Pt–Au–M (M = 3d transition metal) nanoparticles show reduced OH adsorption energies and improved activity for the oxygen reduction reaction (ORR) compared to pure Pt nanoparticles, as obtained by density functional theory.

TD-DFT based fine-tuning of molecular excitation energies using evolutionary algorithms

An evolutionary de novo design method is presented to fine-tune the excitation energies of molecules calculated using time-dependent density functional theory (TD-DFT).

Identifying systematic DFT errors in catalytic reactions

Using CO₂ reduction reactions as examples, we present a widely applicable method for identifying the main source of errors in density functional theory (DFT) calculations.