Atomic Environment Research Articles

Influence of local cation order on the electronic structure and optical properties of cation-disordered semiconductor AgBiS2

Abstract Site disorder exists in some practical semiconductors and can significantly impact their intrinsic properties both beneficially and detrimentally. However, the uncertain local order and structure poses a challenge for both experimental and theoretical research. Especially, it hinders the investigation of the effects of the diverse local atomic environments resulting from the site disorder. In this study, we employ the special quasi-random structure method to perform first-principles research on the connection between local site disorder and electronic/optical properties, using the cation-disordered AgBiS2 (rock salt phase) as an example. We predict that cation-disordered AgBiS2 has a bandgap ranging from 0.6 to 0.8 eV without spin-orbit coupling and that spin-orbit coupling reduces this by approximately 0.3 eV. We observe the effects of local structural features in the disordered lattice, such as the one-dimensional chain-like aggregation of cations that results in the formation of doping energy bands near the band edges, the formation and broadening of band-tail states, and the disturbance in the local electrostatic potential, which significantly reduces the bandgap and stability. The influence of these ordered features on the optical properties is confined to alterations in the bandgap and does not markedly affect the joint density of states or optical absorption. Our study provides a research roadmap for exploring the electronic structure of site-disordered semiconductor materials, suggests that the ordered chain-like aggregation of cations is an effective way to regulate the bandgap of AgBiS2, and provides insight into how variations in local order associated with processing can affect properties.

Bridging Structure- and Ligand-Based Virtual Screening through Fragmented Interaction Fingerprint.

Ligand-based virtual screening (LBVS) and structure-based virtual screening (SBVS), and their combinations, are frequently conducted in modern drug discovery campaigns. As a form of combination, an amalgamation of methods from ligand- and structure-based information, termed hybrid VS approaches, has been extensively investigated such as using interaction fingerprints (IFPs) in combination with machine learning (ML) models. This approach has the potential to prioritize active compounds in terms of protein-ligand binding and ligand structural characteristics, which is assumed to be difficult using either one of the approaches. Herein, we present an IFP, named the fragmented interaction fingerprint (FIFI), for hybrid VS approaches. FIFI is constructed from the extended connectivity fingerprint atom environments of a ligand proximal to the protein residues in the binding site. Each unique ligand substructure within each amino acid residue is encoded as a bit in FIFI while retaining sequence order. From the retrospective evaluation of activity prediction using a limited number and variety of active compounds for six biological targets, FIFI consistently showed higher prediction accuracy than that using previously proposed IFPs. For the same data sets, the screening performance of LBVS, SBVS sequential VS, parallel VS, and other hybrid VS approaches was investigated. Compared to these approaches, FIFI in combination with ML showed overall stable and high prediction accuracy, except for one target: the kappa opioid receptor, where the extended connectivity fingerprint combined with ML models showed better performance than other approaches by wide margins.

Enhancing protein-ligand binding affinity prediction through sequential fusion of graph and convolutional neural networks.

Predicting protein-ligand binding affinity is a crucial and challenging task in structure-based drug discovery. With the accumulation of complex structures and binding affinity data, various machine-learning scoring functions, particularly those based on deep learning, have been developed for this task, exhibiting superiority over their traditional counterparts. A fusion model sequentially connecting a graph neural network (GNN) and a convolutional neural network (CNN) to predict protein-ligand binding affinity is proposed in this work. In this model, the intermediate outputs of the GNN layers, as supplementary descriptors of atomic chemical environments at different levels, are concatenated with the input features of CNN. The model demonstrates a noticeable improvement in performance on CASF-2016 benchmark compared to its constituent CNN models. The generalization ability of the model is evaluated by setting a series of thresholds for ligand extended-connectivity fingerprint similarity or protein sequence similarity between the training and test sets. Masking experiment reveals that model can capture key interaction regions. Furthermore, the fusion model is applied to a virtual screening task for a novel target, PI5P4Kα. The fusion strategy significantly improves the ability of the constituent CNN model to identify active compounds. This work offers a novel approach to enhancing the accuracy of deep learning models in predicting binding affinity through fusion strategies.

N-doped pitch assisted local interface micro-environment tailoring and microcrystalline regulation in carbon nanotube for efficient oxygen reduction reaction

Regulating the atomic micro-environment at active sites and the interfaces between carbon nanoparticles is crucial for enhancing the performance of carbon-based metal-free electrocatalysts. Herein, a novel carbon nanocomposite, nitrogen-doped coal tar pitch composite carbon nanotubes (NPC@CNTs) was proposed, along with the potential synergistic mechanisms at its heterojunction interfaces. The innovative synthesis involves encapsulating carbon nanotubes (CNTs) within coal tar pitch (CTP), followed by nitrogen doping and CO2 activation to form a layered nano-microstructure with hierarchical porosity. This configuration allows for the precise modulation of the atomic environment at the graphitic-N sites within the heterostructure, with the interfacial effects significantly promoting efficient electron transfer and mass transport. The NPC@CNTs exhibit high Eonset(0.991 V verse Reversible Hydrogen Electrode) and Ehalf-wave(0.845 V vs RHE) in alkaline solution. with an OER overpotential of 391 mV at 10 mA cm−2. Furthermore, a Zn–air battery employing NPC@CNTs indicates a peak power density of 91 mW cm−2 with sustained stability over 230 h. Density Functional Theory (DFT) calculations confirmed the heterostructure of graphitic nitrogen-doped CNTs provides a higher density of active sites and more efficient charge transfer at carbon atoms adjacent to nitrogen. The CNTs with functionalized shell-interface has significant potential for bifunctional catalysis, surpassing most heteroatom-doped nanocarbon catalysts and commercial Pt/C (20 %).

Heterointerface engineering of yolk-shell-structured Mn2O3/RuO2 for boosting oxygen electrocatalysis in rechargeable liquid and flexible zinc-air batteries

Rational construction of high-efficient bifunctional oxygen electrocatalysts is of crucial significance for rechargeable Zn-air batteries (ZABs). Here, the yolk-shell-structured Mn2O3/RuO2 heterojunction is purposely constructed via the Kirkendall effect as a robust bifunctional oxygen electrocatalyst for simultaneously driving oxygen evolution and reduction reactions (OER and ORR). The strong interactions between Mn2O3 and RuO2 induces the surface reconstruction that brings the electronic transfer from Mn to Ru, and the changes of coordination environments of metal atoms. Meanwhile, the yolk-shell structure enables maximum contact of the active sites with the electrolyte and prevents the dissolution and peroxidation of active species. As a result, the yolk-shell-structured Mn2O3/RuO2 shows an outstanding bifunctional activity with a very low potential gap of ΔE = 0.60 V. Moreover, the ZABs assembled with Mn2O3/RuO2 render a large open circuit voltage of 1.53 V, a high specific capacity of 742.1 mA h g−1 and an exceptional cycling stability over 850 h that significantly outperforms those of Pt/C+RuO2-based ZABs. Moreover, the self-made flexible solid-state ZAB with Mn2O3/RuO2 cathode also shows good charge–discharge reversibility and flexibility at different bending angles. This work provides a feasible method for the design of efficient oxygen electrocatalysts to promote the practical application of ZABs.

Strong interaction between promoter and metal in Pd-Ba/TiO2 catalysts for formaldehyde oxidation

As our previous works found, alkali metals have a common promotion effect on supported noble metals catalysts for formaldehyde (HCHO) oxidation. As second-group elements, alkaline earth metals (AEMs) are neighbors to the first-group elements and share some properties in common. However, detailed investigations into the specific mechanisms underlying AEMs’ effects on HCHO oxidation remain limited. In this study, we found that Ba addition showed a similar promotion effect on HCHO oxidation for Pd/TiO2. Ba species stabilized Pd groups, improved the dispersion, and even caused a large number of monatomic-like Pd sites to appear, which may be attributed to the electronic interaction between promoter and metal (EIPM) between Ba and Pd. Besides, AEM loading had the important effect of increasing the electron density of metallic Pd nanoparticles, which further improved the ability for O2 activation and so enhanced the mobility of chemisorbed oxygen on the catalyst surface. For Pd/TiO2, the HCHO oxidation path is mainly HCHO→HCOOH→HCOO→H2O+CO2. By contrast, for Pd-Ba/TiO2, with more surface-active species, the formate intermediate was more likely to be directly oxidized into H2O and CO2, which is a more effective reaction pathway. The details of the EIPM between Pd and Ba were investigated by GPAW (DFT calculation module) in ASE (Atomic Simulation Environment). The AEM Ba acted as an electron donor and could interact with Pd d orbital electrons through BaO sp orbital electrons. Ba species were highly dispersed on the carrier due to the Ba-Ti interaction. Ba species dispersed over large areas stabilized the Pd particles and donated electrons to Pd. Therefore, adding an AEM is an efficacious strategy to improve the performance of the catalytic oxidation of HCHO.

Vacancy formation modulating the local atomic environment for remarkable Multi-functional Magnetic properties in Ni–Co–Mn–In alloy

Defect (mainly vacancy) engineering has shown its potential for modulating the functional behavior of magnetic materials. In this work, dense Ni vacancies are introduced in Ni45Mn36.5In13.5Co5 alloy and the effect of these vacancies on the magnetic properties is both theoretically and experimentally clarified. In detail, Ni vacancy with the lowest formation energy is introduced by high energy electron irradiation, which shortens the distance between adjacent Mn atoms to strengthen the interaction between them and increase the corresponding magnetic moments, leading to the dTm/dH increase from −2.89 to 6.28 KT−1. As a result, a breakthrough in the magnetocaloric performance is correspondingly achieved, i.e., the entropy change (ΔSM) increases up to an ultra-high value of 48.15 J/(kg·K), much higher than all other reported values. Moreover, the reversible magnetostrain is also improved from 80 to 200 ppm. In summary, this work provides strong guidance for optimizing the magnetic performance of Ni–Mn based Heusler alloys via vacancy modulation.

New global minimum conformers for the Pt19\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_{19}$$\\end{document} and Pt20\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_{20}$$\\end{document} clusters: low symmetric species featuring different active sites

ContextThe study of platinum (Pt) clusters and nanoparticles is essential due to their extensive range of potential technological applications, particularly in catalysis. The electronic properties that yield optimal catalytic performance at the nanoscale are significantly influenced by the size and structure of Pt clusters. This research aimed to identify the lowest-energy conformers for Pt18\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_{18}$$\\end{document}, Pt19\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_{19}$$\\end{document}, and Pt20\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_{20}$$\\end{document} species using Density Functional Theory (DFT). We discovered new low-symmetry conformers for Pt19\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_{19}$$\\end{document} and Pt20\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_{20}$$\\end{document}, which are 3.0 and 1.0 kcal/mol more stable, respectively, than previously reported structures. Our study highlights the importance of using density functional approximations that incorporate moderate levels of exact Hartree-Fock exchange, alongside basis sets of at least quadruple-zeta quality. The resulting structures are asymmetric with varying active sites, as evidenced by sigma hole analysis on the electrostatic potential surface. This suggests a potential correlation between electronic structure and catalytic properties, warranting further investigation.MethodsAn equivariant graph neural network interatomic potential (NequIP) within the Atomic Simulation Environment suite (ASE) was used to provide initial geometries of the aggregates under study. DFT calculations were performed with the ORCA 5 package, using functional approximations that included Generalized Gradient Approximation (PBE), meta-GGA (TPSS, M06-L), hybrid (PBE0, PBEh), meta-GGA hybrid (TPSSh), and range-separated hybrid (ω\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\omega $$\\end{document}B97x) functionals. Def2-TZVP and Def2-QZVP as well as members of the cc-pwCVXZ-PP family to check basis set convergence were used. QTAIM calculations were performed using the AIMAll suite. Structures were visualized with the AVOGADRO code.

Accelerating Fourth-Generation Machine Learning Potentials Using Quasi-Linear Scaling Particle Mesh Charge Equilibration.

Machine learning potentials (MLPs) have revolutionized the field of atomistic simulations by describing atomic interactions with the accuracy of electronic structure methods at a small fraction of the cost. Most current MLPs construct the energy of a system as a sum of atomic energies, which depend on information about the atomic environments provided in the form of predefined or learnable feature vectors. If, in addition, nonlocal phenomena like long-range charge transfer are important, fourth-generation MLPs need to be used, which include a charge equilibration (Qeq) step to take the global structure of the system into account. This Qeq can significantly increase the computational cost and thus can become a computational bottleneck for large systems. In this Article, we present a highly efficient formulation of Qeq that does not require the explicit computation of the Coulomb matrix elements, resulting in a quasi-linear scaling method. Moreover, our approach also allows for the efficient calculation of energy derivatives, which explicitly consider the global structure-dependence of the atomic charges as obtained from Qeq. Due to its generality, the method is not restricted to MLPs and can also be applied within a variety of other force fields.

A “bond-focused” local atomic environment representation for a high throughput solute interaction spectrum analysis

Local atomic environment (LAE) representations have enabled a deeper understanding of complex alloy configurations, by enabling machine learning and data science approaches to previously inaccessible tasks. For example, recent developments have used site-centered descriptors to interpret the structurally complex LAEs present in polycrystalline grain boundary (GB) networks and learn the spectrum of grain boundary segregation energies available across many binary alloys. One limitation of many LAE representations is their “site-focus”, which limits their ability to capture chemical complexity at the atomic scale. This paper introduces a modified “bond-focused” LAE representation, which is used to assess pair-wise solute interactions in the topologically complex environments of grain boundaries. This approach opens the pathway to learning models, which we develop here to construct a large-scale database of spectral parameters describing grain boundaries in binary alloys.

Probing the Depths of Electrocatalysis Systems: X-Ray Absorption Spectroscopy Reveals Catalyst Behavior

X-ray absorption spectroscopy (XAS) is a powerful experimental technique used to study the electronic structure and local environment of atoms in a material. It provides valuable insights into the chemical bonding, oxidation states, and coordination geometry of elements. XAS has been widely applied in various fields of electrochemistry, including popular topics like the hydrogen evolution reaction (HER), oxygen reduction reaction (ORR), and CO2 reduction reaction (CO2RR).Despite the growing recognition of the practical importance of XAS techniques, correlating experimental spectroscopy with the working mechanisms and deciphering the structure of the electrochemical interface under reaction conditions remains challenging. In the case of CO2RR, we utilized operando XAS to investigate multiple Cu-based catalysts in different cell configurations. Our findings revealed that altering the reaction environment can induce an alternative pathway for catalyst structural changes. Moreover, operando XAS demonstrated that the reduction of Cu oxide catalysts is not solely governed by applied potentials but is also influenced by mass transport and other parameters. For ORR, in situ XAS not only provides information about the local environment changes of Pt-based catalysts but also unveils surface properties through △μ-X-ray adsorption near-edge spectroscopy (△μ-XANES). This further aids in the discovery of degradation mechanisms in proton exchange membrane fuel cells (PEMFCs). In the case of HER, in situ XAS enables characterization beyond catalysts and extends to the electrochemical interface.Overall, XAS techniques play a crucial role in understanding the electronic structure and local environment of atoms in electrochemical systems, shedding light on reaction mechanisms and catalyst performance under realistic conditions Acknowledgement The authors thank the funding source from Liquid Sunlight Alliance under the U.S. DOE, Office of Science, Office of Basic Energy Sciences, Fuels Award Number DE-SC0021266 and the Laboratory Directed Research and Development Program of the Lawrence Berkeley National Laboratory under the U.S. DOE Contract No. DE-AC02-05CH11231.

Crystal structure identification with 3D convolutional neural networks with application to high-pressure phase transitions in SiO2

Efficient, reliable and easy-to-use structure recognition of atomic environments is essential for the analysis of atomic scale computer simulations. In this work, we train two neuronal network (NN) architectures, namely PointNet and dynamic graph convolutional NN (DG-CNN) using different hyperparameters and training regimes to assess their performance in structure identification tasks of atomistic structure data. We show benchmarks on simple crystal structures, where we can compare against established methods. The approach is subsequently extended to structurally more complex SiO2 phases. By making use of this structure recognition tool, we are able to achieve a deeper understanding of the crystallization process in amorphous SiO2 under shock compression. Lastly, we show how the NN based structure identification workflows can be integrated into OVITO using its python interface.

First-principles study of stability, order and disorder based on an entropy descriptor in noble and ferromagnetic transition metal alloys

Binary alloys AB composed of ferromagnetic metals A (Fe, Co, Ni) and the late 4d−5d noble metals B (Rh, Pd, Ag, Ir, Pt, Au) have been investigated using density functional theory with the generalized gradient approximation to understand the role of magnetism in the stability and the order–disorder transition which has an impact on their physicochemical properties, their applications and their possible implementation as precursors of high-entropy alloys. The enthalpy of formation related to the stability demonstrates that all the alloys are more stable in the ferromagnetic phase than in the nonmagnetic phase. The transition from ordered to disordered phases is quantified using a descriptor which is the standard deviation of the energy spectrum of a set of small nanoalloys with random atomic configurations. The study highlights the fact that the entropy-related descriptor, which is a quantity in determining the formation of a disordered phase as a solid solution or an ordered phase is highly dependent on the atomic environment. Despite the fact that the overall variation of this descriptor is supposed to be unpredictable, there is a noticeable trend showing that the environment-dependent ferromagnetism contributes to a chemical order in alloys and nanoalloys and that this order depends on the atomic radius of the species considered. The results indicate that species with small atomic radii, such as nickel, rhodium or iridium are more likely to form solid solutions than species with larger atomic radii and with more delocalized orbitals.

Regulating Second-Shell Coordination in Cobalt Single-Atom Catalysts toward Highly Selective Hydrogenation.

Manipulating the local coordination environment of central metal atoms in single-atom catalysts (SACs) is a powerful strategy to exploit efficient SACs with optimal electronic structures for various applications. Herein, Co-SACs featured by Co single atoms with coordinating S atoms in the second shell dispersed in a nitrogen-doped carbon matrix have been developed toward the selective hydrogenation of halo-nitrobenzene. The location of the S atom in the model Co-SAC is verified through synchrotron-based X-ray absorption spectroscopy and theoretical calculations. The resultant Co-SACs containing second-coordination shell S atoms demonstrate excellent activity and outstanding durability for selective hydrogenation, superior to most precious metal-based catalysts. In situ characterizations and theoretical results verify that high activity and selectivity are attributed to the advantageous formation of the Co-O bond between p-chloronitrobenzene and Co atom at Co1N4-S moieties and the lower free energy and energy barriers of the reaction. Our findings unveil the correlation between the performance and second-shell coordination atom of SACs.

Coordination Environment and Distance Optimization of Dual Single Atoms on Fluorine-Doped Carbon Nanotubes for Chlorine Evolution Reaction.

The chlorine evolution reaction (CER) is a crucial anode reaction in the chlor-alkali industrial process. Precious metal-based dimensionally stable anodes (DSAs) are commonly used as catalysts for CER but are constrained by their high cost and low selectivity. Herein, a Pt dual singe-atom catalyst (DSAC) dispersed on fluorine-doped carbon nanotubes (F-CNTs) is designed for an efficient and robust CER process. The prepared Pt DSAC demonstrates excellent CER activity with a low overpotential of 21 mV to achieve a current density of 10 mA cm-2 and a remarkable mass activity of 3802.6 A gpt-1 at an overpotential around 30 mV, outperforming those of commercial DSA and Pt single-atom catalysts. The excellent CER performance of Pt DSAC is attributed to the high atomic utilization and improved intrinsic activity. Notably, introducing fluorine atoms on CNTs increases the oxidation and chlorination resistance of Pt DSAC, and reduces the demetalization ratio of Pt atoms, resulting in excellent long-term CER stability. Theoretical calculations reveal that several Pt DSAC configurations with optimized first-shell ligands and interatomic distance display lower energy barriers for Cl intermediates generation and weaker ionic Pt-Cl bond interaction, which are favorable for the CER process.

Covalent Transition Metal Borosilicides: Reaction Pathways in Molten Salts for Water Oxidation Electrocatalysis.

The properties of transition metal borides and silicides are intimately linked to the covalent character of the chemical bonds within their crystal structures. Bringing boron and silicon together within metal borosilicides can then engender different competing covalent networks and complex charge distributions. This situation results in unique structures and atomic environments, which can impact charge transport and catalytic properties. Metal borosilicides, however, hold the status of unusual exotic species, difficult to synthesize and with poor knowledge of their properties. Our strategy consists of developing a redox pathway to synthesize transition metal borosilicides in inorganic molten salts as high-temperature solvents. By studying the formation of Ni6Si2B, Co4.75Si2B, Fe5SiB2, and Mn5SiB2 with in situ X-ray diffraction, we highlight how new reaction routes, maintaining covalent structural building blocks, draw a general scheme of their formation. This pathway is driven by the covalence of the chemical bonds within the boron coordination framework. Next, we demonstrate high efficiency for water oxidation electrocatalysis, especially for Ni6Si2B. We ascribe the strongly increased resistance to corrosion, high stability, and electrocatalytic activity of the Ni6Si2B-derived material to three factors: (1) the two entangled boron and silicon covalent networks; (2) the ability to codope with boron and silicon an in situ generated catalytic layer; and (3) a rare electron enrichment of the transition metal by back-donation from boron atoms, previously unknown within this compound family. With this work, we then unveil a new chemical dimension for Earth-abundant water oxidation electrocatalysts by bringing to light a new family of materials.

Wigner kernels: Body-ordered equivariant machine learning without a basis.

Machine-learning models based on a point-cloud representation of a physical object are ubiquitous in scientific applications and particularly well-suited to the atomic-scale description of molecules and materials. Among the many different approaches that have been pursued, the description of local atomic environments in terms of their discretized neighbor densities has been used widely and very successfully. We propose a novel density-based method, which involves computing "Wigner kernels." These are fully equivariant and body-ordered kernels that can be computed iteratively at a cost that is independent of the basis used to discretize the density and grows only linearly with the maximum body-order considered. Wigner kernels represent the infinite-width limit of feature-space models, whose dimensionality and computational cost instead scale exponentially with the increasing order of correlations. We present several examples of the accuracy of models based on Wigner kernels in chemical applications, for both scalar and tensorial targets, reaching an accuracy that is competitive with state-of-the-art deep-learning architectures. We discuss the broader relevance of these findings to equivariant geometric machine-learning.

Toward rationally designing high-performance perovskite sensing material: Role of dielectric constant and crystal symmetry

P-type oxide semiconductor gas sensors have gained increasing attention for their distinctive catalytic activity, high oxygen adsorption, and high humidity resistance. However, the low gas response of p-type sensors restricts their utilization, prompting numerous attempts to overcome this limitation. In this study, we have successfully engineered a promising p-type gas sensor by increasing the dielectric constant and optimizing crystal symmetry, departing from conventional approaches such as reducing particle size, constructing complex heterojunctions, and decorating with noble metals. The as-prepared multiferroic Bi0.92Dy0.8FeO3 gas sensor exhibits a well-defined gas response to acetone, demonstrating ultralow detection limits, distinctive selectivity, high humidity resistance, and long-term stability. Through comparisons of crystal symmetry, morphology, specific surface area, atomic chemical environment, dielectric properties, and sensing properties among a series of Dy3+ doped BiFeO3 solid solutions, we attribute this extraordinary sensing performance of Bi0.92Dy0.8FeO3 to its higher dielectric constant and unique domain-wall structure arising from its non-centrosymmetric crystal structure. This work provides additional perspective for preparing high-performance gas sensors.

A simple recipe for designing multicomponent ultra-high temperature ceramic classes by using structure maps coupled with machine learning

Successful synthesis of novel high entropy ceramic (HEC) for ultra-high temperature application classes, namely, borides, carbides, and nitrides, has been experiencing a bottleneck in having a suitable design and successful synthesis strategy. Producing high-entropy ultra-high-temperature ceramics from their oxides offers a major processing benefit, while employing a design approach using machine learning enhances the efficiency of the formation of single-phase HECs. In this regard, we propose a generalized strategy to generate a semi-synthetic database for each of these classes using literature data and atomic environment mapping-based structure plots, which can further be used to build machine learning models. The imbalance of the dataset was addressed using adaptive synthetic sampling and the edited nearest neighbors technique. The trained models are able to accurately predict over 90% of the single-phase chemistry for each of the classes. Furthermore, a few compositions representing these classes were successfully synthesized from the corresponding oxide mixture to validate the effectiveness of the proposed strategy.

Cartesian atomic cluster expansion for machine learning interatomic potentials

Machine learning interatomic potentials are revolutionizing large-scale, accurate atomistic modeling in material science and chemistry. Many potentials use atomic cluster expansion or equivariant message-passing frameworks. Such frameworks typically use spherical harmonics as angular basis functions, followed by Clebsch-Gordan contraction to maintain rotational symmetry. We propose a mathematically equivalent and simple alternative that performs all operations in the Cartesian coordinates. This approach provides a complete set of polynormially independent features of atomic environments while maintaining interaction body orders. Additionally, we integrate low-dimensional embeddings of various chemical elements, trainable radial channel coupling, and inter-atomic message passing. The resulting potential, named Cartesian Atomic Cluster Expansion (CACE), exhibits good accuracy, stability, and generalizability. We validate its performance in diverse systems, including bulk water, small molecules, and 25-element high-entropy alloys.