Rb-Doped Perovskite Oxides: Surface Enrichment and Structural Reconstruction During the Oxygen Evolution Reaction.

Alkali-metal doped perovskite oxides have emerged as promising materials due to their unique properties and broad applications in various fields, including photovoltaics and catalysis. Understanding the complex interplay between alkali metal doping, structural modifications, and their impact on performance remains a crucial challenge. In this study, this challenge is addressed by investigating the synthesis and properties of Rb-doped perovskite oxides. These results reveal that the doping of Rb into perovskite oxides function as a structural modifier in the as-synthesized samples and during the oxygen evolution reaction (OER) as well. Electron microscopy and first-principles calculations confirm the enrichment of Rb on the surface of the as-synthesized sample. Further investigations into the electrocatalytic reaction revealed that the Rb-doped perovskite underwent drastic restructuring with Rb leaching and formation of strontium oxide.

Vacancy Ordering in Ultrathin Copper Oxide Films on Cu(111).

Oxide thin films grown on metal surfaces have wide applications in catalysis and beyond owing to their unique surface structures compared to their bulk counterparts. Despite extensive studies, the atomic structures of copper surface oxides on Cu(111), commonly referred to as "44" and "29", have remained elusive. In this work, we demonstrated an approach for the structural determination of oxide surfaces using element-specific scanning tunneling microscopy (STM) imaging enhanced by functionalized tips. This approach enabled us to resolve the atomic structures of "44" and "29" surface oxides, which were further corroborated by noncontact atomic force microscopy (nc-AFM) measurements and Monte Carlo (MC) simulations. The stoichiometry of the "44" and "29" frameworks was identified as Cu23O16 and Cu16O11, respectively. Contrary to the conventional hypothesis, we observed ordered Cu vacancies within the "44" structure manifesting as peanut-shaped cavities in the hexagonal lattice. Similarly, a combination of Cu and O vacancies within the "29" structure leads to bean-shaped cavities within the pentagonal lattice. Our study has thus resolved the decades-long controversy on the atomic structures of "44" and "29" surface oxides, advancing our understanding of copper oxidation processes and introducing a robust framework for the analysis of complex oxide surfaces.

High-Quality Data Enabling Universality of Band Gap Descriptor and Discovery of Photovoltaic Perovskites.

Extensive machine-learning-assisted research has been dedicated to predicting band gaps for perovskites, driven by their immense potential in photovoltaics. Yet, the effectiveness is often hampered by the lack of high-quality band gap data sets, particularly for perovskites involving d orbitals. In this work, we consistently calculate a large data set of band gaps with a high level of accuracy, which is rigorously validated by experimental and state-of-the-art GW band gaps. Leveraging this achievement, our machine-learning-derived descriptor exhibits exceptional universality and robustness, proving effectiveness not only for single and double, halide and oxide perovskites regardless of the underlying atomic structures but also for hybrid organic-inorganic perovskites. With this approach, we comprehensively explore up to 15,659 materials, unveiling 14 unreported lead-free perovskites with suitable band gaps for photovoltaics. Notably, MASnBr3, FA2SnGeBr6, MA2AuAuBr6, FA2AuAuBr6, FA2InBiCl6, FA2InBiBr6, and Ba2InBiO6 stand out with direct band gaps, small effective masses, low exciton binding energies, and high stabilities.

Structure Sensitivity of Metal Catalysts Revealed by Interpretable Machine Learning and First-Principles Calculations.

The nature of the active sites and their structure sensitivity are the keys to rational design of efficient catalysts but have been debated for almost one century in heterogeneous catalysis. Though the Brønsted-Evans-Polanyi (BEP) relationship along with linear scaling relation has long been used to study the reactivity, explicit geometry, and composition properties are absent in this relationship, a fact that prevents its exploration in structure sensitivity of supported catalysts. In this work, based on interpretable multitask symbolic regression and a comprehensive first-principles data set, we discovered a structure descriptor, the topological under-coordinated number mediated by number of valence electrons and the lattice constant, to successfully address the structure sensitivity of metal catalysts. The database used for training, testing, and transferability investigation includes bond-breaking barriers of 20 distinct chemical bonds over 10 transition metals, two metal crystallographic phases, and 17 different facets. The resulting 2D descriptor composing the structure term and the reaction energy term shows great accuracy to predict the reaction barriers and generalizability over the data set with diverse chemical bonds in symmetry, bond order, and steric hindrance. The theory is physical and concise, providing a constructive strategy not only to understand the structure sensitivity but also to decipher the entangled geometric and electronic effects of metal catalysts. The insights revealed are valuable for the rational design of the site-specific metal catalysts.

Distilling Accurate Descriptors from Multi-Source Experimental Data for Discovering Highly Active Perovskite OER Catalysts.

Perovskite oxides are promising catalysts for the oxygen evolution reaction, yet the huge chemical space remains largely unexplored due to the lack of effective approaches. Here, we report the distilling of accurate descriptors from multi-source experimental data for accelerated catalyst discovery by using the newly designed method of sign-constrained multi-task learning within the framework of sure independence screening and sparsifying operator that overcomes the challenge of data inconsistency between different sources. While many previous descriptors for the catalytic activity were proposed based on respective small data sets, we obtained a new 2D descriptor (dB, nB) based on 13 experimental data sets collected from different publications. Great universality and predictive accuracy, and the bulk-surface correspondence, of this descriptor have been demonstrated. With this descriptor, hundreds of unreported candidate perovskites with activity greater than the benchmark catalyst Ba0.5Sr0.5Co0.8Fe0.2O3 were identified from a large chemical space. Our experimental validations on five candidates confirmed the three highly active perovskite catalysts SrCo0.6Ni0.4O3, Rb0.1Sr0.9Co0.7Fe0.3O3, and Cs0.1Sr0.9Co0.4Fe0.6O3. This work provides an important new approach in dealing with inconsistent multi-source data for applications in the field of data-driven catalysis and beyond.

Architecture Design and Catalytic Activity: Non-Noble Bimetallic CoFe/fe3 O4 Core-Shell Structures for CO2 Hydrogenation.

Non-noble metal catalysts now play a key role in promoting efficiently and economically catalytic reduction of CO2 into clean energy, which is an important strategy to ameliorate global warming and resource shortage issues. Here, a non-noble bimetallic catalyst of CoFe/Fe3 O4 nanoparticles is successfully designed with a core-shell structure that is well dispersed on the defect-rich carbon substrate for the hydrogenation of CO2 under mild conditions. The catalysts exhibit a high CO2 conversion activity with the rate of 30% and CO selectivity of 99%, and extremely robust stability without performance decay over 90 h in the reverse water gas shift reaction process. Notably, it is found that the reversible exsolution/dissolution of cobalt in the Fe3 O4 shell will lead to a dynamic and reversible deactivation/regeneration of the catalysts, accompanying by shell thickness breathing during the repeated cycles, via atomic structure study of the catalysts at different reaction stages. Combined with density functional theory calculations, the catalytic activity reversible regeneration mechanism is proposed. This work reveals the structure-property relationship for rational structure design of the advanced non-noble metallic catalyst materials with much improved performance.

Improving Symbolic Regression for Predicting Materials Properties with Iterative Variable Selection.

Symbolic regression offers a promising avenue for describing the structure-property relationships of materials with explicit mathematical expressions, yet it meets challenges when the key variables are unclear because of the high complexity of the problems. In this work, we propose to solve the difficulty by automatically searching for important variables from a large pool of input features. A new algorithm that integrates symbolic regression with iterative variable selection (VS) was designed for optimization of the model with a large amount of input features. Using the recent method SISSO for symbolic regression and random search for variable selection, we show that the VS-assisted SISSO (VS-SISSO) can effectively manage even hundreds of input features that the SISSO alone was computationally hindered, and it fastly converges to (near) optimal solutions when the model complexity is not high. The efficiency of this approach for improving the accuracy of symbolic regression in materials science was demonstrated in the two showcase applications of learning approximate equations for the band gap of inorganic halide perovskites and the stability of single-atom alloy catalysts.

Machine Learning and Structural Design to Optimize the Flame Retardancy of Polymer Nanocomposites with Graphene Oxide Hydrogen Bonded Zinc Hydroxystannate.

Designing flame-retardant polymers with high performance is a long-standing challenge, partly because of the time-consuming traditional approaches based on experiential intuition and trial-and-error screenings. Inspired by the effective new paradigm of data-driven material discovery, we used machine learning to analyze experimental data to accelerate the development of new flame-retardant polymers. To explore the relationship between limit oxygen index (LOI) and components, we prepared 20 composites and then trained a simple equation for the LOI using the method sure independence screening and sparsifying operator (SISSO). The data analysis allows us for a better understanding of the flame-retardant mechanism and components, and the equation has good accuracy in guiding the design of composites with high flame-retardant performance. Meanwhile, the increasing structural design of flame retardants is crucial to flame-retardant polymer composites. We proposed a structure of nano graphene oxide (GO) wrapped micro zinc hydroxystannate (ZHS) in a simple but effective way as a novel flame-retardant agent to enhance the flame retardancy and mechanical properties of polypropylene (PP) composites. The GO sheets were like "light yarns" wrapped onto the ZHS via hydrogen bonding in an ethanol solution. The selected samples were analyzed to confirm the predictive LOI model. The resultant composites with the substitution of intumescent flame retardant (IFR) by 1.0, 2.0, and 4.0 wt % ZHS@GO conferred better flame retardancy compared with PP composite containing only IFR, reflected by the efficient increase of LOI value and V0 rating of UL-94 vertical tests. The analysis principles and facile fabrication strategies proposed in this work could be important for developing highly flame retardant composites.

Prediction and Classification of Formation Energies of Binary Compounds by Machine Learning: An Approach without Crystal Structure Information.

It is well believed that machine learning models could help to predict the formation energies of materials if all elemental and crystal structural details are known. In this paper, it is shown that even without detailed crystal structure information, the formation energies of binary compounds in various prototypes at the ground states can be reasonably evaluated using machine-learning feature abstraction to screen out the important features. By combining with the "white-box" sure independence screening and sparsifying operator (SISSO) approach, an interpretable and accurate formation energy model is constructed. The predicted formation energies of 183 experimental and 439 calculated stable binary compounds (E hull = 0) are predicted using this model, and both show reasonable agreements with experimental and Materials Project's calculated values. The descriptor set is capable of reflecting the formation energies of binary compounds and is also consistent with the common understanding that the formation energy is mainly determined by electronegativity, electron affinity, bond energy, and other atomic properties. As crystal structure parameters are not necessary prerequisites, it can be widely applied to the formation energy prediction and classification of binary compounds in large quantities.

Single-atom alloy catalysts designed by first-principles calculations and artificial intelligence.

Single-atom-alloy catalysts (SAACs) have recently become a frontier in catalysis research. Simultaneous optimization of reactants' facile dissociation and a balanced strength of intermediates' binding make them highly efficient catalysts for several industrially important reactions. However, discovery of new SAACs is hindered by lack of fast yet reliable prediction of catalytic properties of the large number of candidates. We address this problem by applying a compressed-sensing data-analytics approach parameterized with density-functional inputs. Besides consistently predicting efficiency of the experimentally studied SAACs, we identify more than 200 yet unreported promising candidates. Some of these candidates are more stable and efficient than the reported ones. We have also introduced a novel approach to a qualitative analysis of complex symbolic regression models based on the data-mining method subgroup discovery. Our study demonstrates the importance of data analytics for avoiding bias in catalysis design, and provides a recipe for finding best SAACs for various applications.

Wall Density-Controlled Thermal Conductive and Mechanical Properties of Three-Dimensional Vertically Aligned Boron Nitride Network-Based Polymeric Composites.

Polymeric composites with good thermal conductive and improved mechanical properties are in high demand in the thermal management materials. Construction of a three-dimensional (3D) structure has been proved to be an effective method to obtain polymeric composites with improved through-plane thermal conductivity (TC) for efficient thermal management of electronics. However, the TC enhancement of the obtained polymeric composites is limited, mainly due to poor control of the 3D thermal conductive network. Additionally, achieving high thermal conductive properties and enhanced mechanical properties simultaneously is of great challenge for polymeric composites. In this work, a 3D boron nitride framework (BNF) with a well-defined vertically aligned open structure and designed wall density fabricated by a unidirectional freezing technique was applied. The as-prepared BNF/polyethylene glycol (PBNF) composites exhibit enhanced through-plane TC, excellent thermal transfer capability (ΔTmax = 34 °C), and improved mechanical properties (Young's modulus enhancement up to 356%) simultaneously, making it attractive to thermal management applications. Strong correlation between the TC and mechanical properties of the PBNF composites and the wall density of the BNF scaffolds was found, providing opportunities to tune the TC and mechanical properties through the controlling of wall density. Furthermore, the models between TC and Young's modulus of PBNF composites were established by using the data-driven method "sure independence screening and sparsifying operator", which enables us to predict TC and Young's modulus of the polymeric composites for designing promising composite materials. The design principles and fabrication strategies proposed in this work could be important for developing advanced composite materials.

New tolerance factor to predict the stability of perovskite oxides and halides.

Predicting the stability of the perovskite structure remains a long-standing challenge for the discovery of new functional materials for many applications including photovoltaics and electrocatalysts. We developed an accurate, physically interpretable, and one-dimensional tolerance factor, τ, that correctly predicts 92% of compounds as perovskite or nonperovskite for an experimental dataset of 576 ABX 3 materials (X = O2-, F-, Cl-, Br-, I-) using a novel data analytics approach based on SISSO (sure independence screening and sparsifying operator). τ is shown to generalize outside the training set for 1034 experimentally realized single and double perovskites (91% accuracy) and is applied to identify 23,314 new double perovskites (A 2 BB'X 6) ranked by their probability of being stable as perovskite. This work guides experimentalists and theorists toward which perovskites are most likely to be successfully synthesized and demonstrates an approach to descriptor identification that can be extended to arbitrary applications beyond perovskite stability predictions.

Global minimization of gold clusters by combining neural network potentials and the basin-hopping method.

Neural network potentials trained by first-principles density functional theory total energies were applied to search for global minima of gold nanoclusters within the basin-hopping method. Using Au58 as an example, we found a new putative global minimum which has a core-shell structure of Au10@Au48 and C4 symmetry. This new structure of Au58 is 0.24 eV per formula more stable than the best previous model that has C1 symmetry. This work demonstrates that neural network potentials combined with the basin-hopping method could be very useful in global minimization for medium-sized metal clusters which might be computationally prohibitive for first principles density functional theory.

Intermixed adatom and surface-bound adsorbates in regular self-assembled monolayers of racemic 2-butanethiol on Au(111).

In situ scanning tunneling microscopy combined with density functional theory molecular dynamics simulations reveal a complex structure for the self-assembled monolayer (SAM) of racemic 2-butanethiol on Au(111) in aqueous solution. Six adsorbate molecules occupy a (10×√3)R30° cell organized as two RSAuSR adatom-bound motifs plus two RS species bound directly to face-centered-cubic and hexagonally close-packed sites. This is the first time that these competing head-group arrangements have been observed in the same ordered SAM. Such unusual packing is favored as it facilitates SAMs with anomalously high coverage (30%), much larger than that for enantiomerically resolved 2-butanethiol or secondary-branched butanethiol (25%) and near that for linear-chain 1-butanethiol (33%).

The ligand effect on the isomer stability of Au24(SR)20 clusters.

A key challenge in nanocluster research in particular and nanoscience in general is structure prediction for known compositions. Usually a simple ligand such as a methyl group is used to replace complex ligands in structure prediction of ligand-protected nanoclusters. However, how ligands dictate the energy landscape of such a cluster remains unclear. Here we elucidate the role of the ligand effect on the isomer stability of Au24(SR)20 nanoclusters by computing the relative energy of two isomers (one from the experiment, denoted as the "J" isomer; the other is the best theoretical model, denoted as the "P" isomer) of Au24(SR)20 with dispersion-corrected density functional theory. We find that when R = -CH3, the two isomers are equally stable (within 0.13 eV), but for R = -CH2CH2Ph the P isomer is more stable by 1.6 eV and for R = -CH2Ph-(t)Bu the J isomer is more stable by 1.0 eV. Partition of the total energy into DFT and vdW contributions indicates that the higher stability of the P isomer in the case of R = -CH2CH2Ph stems from the stronger vdW interactions among -CH2CH2Ph groups, while the higher stability of the J isomer in the case of R = -CH2Ph-(t)Bu is due to its better capacity to respond to the steric effect of the larger -CH2Ph-(t)Bu groups. This finding confirms that the ligand plays a crucial role in dictating the isomer stability.

Controlling the stereochemistry and regularity of butanethiol self-assembled monolayers on au(111).

The rich stereochemistry of the self-assembled monolayers (SAMs) of four butanethiols on Au(111) is described, the SAMs containing up to 12 individual C, S, or Au chiral centers per surface unit cell. This is facilitated by synthesis of enantiomerically pure 2-butanethiol (the smallest unsubstituted chiral alkanethiol), followed by in situ scanning tunneling microscopy (STM) imaging combined with density functional theory molecular dynamics STM image simulations. Even though butanethiol SAMs manifest strong headgroup interactions, steric interactions are shown to dominate SAM structure and chirality. Indeed, steric interactions are shown to dictate the nature of the headgroup itself, whether it takes on the adatom-bound motif RS(•)Au(0)S(•)R or involves direct binding of RS(•) to face-centered-cubic or hexagonal-close-packed sites. Binding as RS(•) produces large, organizationally chiral domains even when R is achiral, while adatom binding leads to rectangular plane groups that suppress long-range expression of chirality. Binding as RS(•) also inhibits the pitting intrinsically associated with adatom binding, desirably producing more regularly structured SAMs.

Atomistic theory of Ostwald ripening and disintegration of supported metal particles under reaction conditions.

Understanding Ostwald ripening and disintegration of supported metal particles under operating conditions has been of central importance in the study of sintering and dispersion of heterogeneous catalysts for long-term industrial implementation. To achieve a quantitative description of these complicated processes, an atomistic and generic theory taking into account the reaction environment, particle size and morphology, and metal-support interaction is developed. It includes (1) energetics of supported metal particles, (2) formation of monomers (both the metal adatoms and metal-reactant complexes) on supports, and (3) corresponding sintering rate equations and total activation energies, in the presence of reactants at arbitrary temperature and pressure. The thermodynamic criteria for the reactant assisted Ostwald ripening and induced disintegration are formulated, and the influence of reactants on sintering kinetics and redispersion are mapped out. Most energetics and kinetics barriers in the theory can be obtained conveniently by first-principles theory calculations. This allows for the rapid exploration of sintering and disintegration of supported metal particles in huge phase space of structures and compositions under various reaction environments. General strategies of suppressing the sintering of the supported metal particles and facilitating the redispersions of the low surface area catalysts are proposed. The theory is applied to TiO(2)(110) supported Rh particles in the presence of carbon monoxide, and reproduces well the broad temperature, pressure, and particle size range over which the sintering and redispersion occurred in such experiments. The result also highlights the importance of the metal-carbonyl complexes as monomers for Ostwald ripening and disintegration of supported metal catalysts in the presence of CO.

CO oxidation at the perimeters of an FeO/Pt(111) interface and how water promotes the activity: a first-principles study.

The catalytic role of the Pt--Fe cation ensemble presented at the perimeters of the FeO film supported on Pt(111) for low-temperature CO oxidation and the promotion of water on activity were studied by using DFT calculations. We found that the perimeter sites along the edge of the FeO islands on Pt provided a favorable ensemble that consisted of coordinatively unsaturated ferrous species and nearby Pt atoms for O(2) and H(2) O activation free from CO poison. A dissociative oxygen atom at the Pt--Fe cation ensemble reacts easily with CO adsorbed on nearby Pt. The OH group from water dissociation not only facilitates activation of the oxygen molecule, more importantly it opens a facile reaction channel for CO oxidation through the formation of the carboxyl intermediate. The presence of the OH group on the FeO film strengthens interfacial interactions between FeO and Pt(111), which would make the FeO film more resistant to further oxidation. The importance of the Pt--Fe cation ensemble and the role of water as a cocatalyst for low-temperature CO oxidation is highlighted.