Atomic Environment Research Articles

Tailoring the Coordination Environment of Cu Single Atoms for Achieving Regioselective C–C Bond Activation of Amides

Tailoring the Coordination Environment of Cu Single Atoms for Achieving Regioselective C–C Bond Activation of Amides

Spectral operator representations

Machine learning in atomistic materials science has grown to become a powerful tool, with most approaches focusing on atomic geometry, typically decomposed into local atomic environments. This approach, while well-suited for machine-learned interatomic potentials, is conceptually at odds with learning complex intrinsic properties of materials, often driven by spectral properties commonly represented in reciprocal space (e.g., band gaps or mobilities) which cannot be readily partitioned in real space. For such applications, methods that represent the electronic rather than the atomic structure could be more promising. In this work, we present a general framework focused on electronic-structure descriptors that take advantage of the natural symmetries and inherent interpretability of physical models. We apply this framework first to material similarity and then to accelerated screening, where a model trained on 217 materials correctly labels 75% of entries in the Materials Cloud 3D database, which meet common screening criteria for promising transparent-conducting materials.

Ductilization of single-phase refractory high-entropy alloys via activation of edge dislocation

Refractory high entropy alloys (RHEAs) are of particular interest for their superior high-temperature strength. However, most of RHEAs are suffered from the brittleness especially at room temperature which hindered their applications. Here, we choose TiZrHfNbx (x=0.4, 0.6, 0.8, 1) as a model alloy system in which all the alloys exhibited a single-phase BCC structure. With the increasing Nb content, the fracture strain significantly increased from 8.76% to 33.21% without sacrificing the overall yield strength. The dislocation interactions were systematically investigated through extensive experimental and theoretical approaches. The limited tensile ductility of TiZrHfNb0.4 was attributed to the long straight screw dislocations confined in a few slip planes. In sharp contrast, the dislocations in TiZrHfNb alloy are rather homogeneously distributed and the superior tensile ductility of TiZrHfNb alloy is due to the activation of edge dislocations which is rare in ordinary BCC structured alloys. The atomic scale physical origin of different dislocation configuration and behaviors can be attributed to the higher misfit volume and elastic asymmetry of TiZrHfNb alloy. The outcome of this research not only reveal a new ductilization mechanism but also provide a new pathway to design ductile RHEAs through regulation the atomic scale environment.

An explicitly magnetic modified embedded atom method formalism for coupled spin dynamics and molecular dynamics

Abstract In this paper, we augment the modified embedded atom method formalism to include magnetic spin-spin interactions for elements with a persistent magnetic moment. While previous spin coupling methods have been based on pair potentials, our Magnetic MEAM formalism, which we term MagMEAM, incorporates the many-body and angular effects of MEAM allowing for the strength of the magnetic interaction to vary with atomic environment. In particular, this allows potentials using this formalism to differentiate the magnetic interaction of different stable phases of magnetic elements such as the ferritic and austenitic phases of iron. This, in turn, allows for a more robust and realistic description of magnetism in polymorphic materials than was previously possible. The motivation for MagMEAM, including the insufficiency of magnetic pair potentials, is presented and the structure of the formalism is developed. A sample iron potential is developed using this formalism and shown to exceed the capabilities of existing magnetic pair potentials by simultaneously reproducing the magnetic energy of both martensite and austenite as well as the dynamic mechanical and magnetic properties of martensite. This newly designed formalism will allow for deeper explorations in the the complex interaction between different phases of polymorphic magnetic materials at the molecular dynamics scale.

Reactions of a Prototypical Phenolic Antioxidant with Radicals in Polyethylene: Insights from Density Functional Theory.

Phenolic antioxidants are widely used to prevent oxidation, which is the main degradation process for many polymers, in particular polyolefins among which polyethylene is the most employed one. Although it is generally understood that one of the main mechanisms by which phenolic antioxidants prevent or slow down oxidation is by deactivating radicals and preventing the formation of alkyl radicals, detailed understanding at the atomic scale of the hierarchy of radical reactions is still lacking. Here, we investigate the interaction of a prototypical phenolic antioxidant, butylated hydroxytoluene (BHT), with radicals in a polyethylene model by means of static and dynamic simulations based on density functional theory. We focus on the H-transfer reactions between BHT and radical species by evaluating the associated energy barriers and analyze the conditions in which these relevant reactions occur by first-principles molecular dynamics simulation. Our polyethylene model includes a realistic surface of a crystalline lamella, thus describing the local atomic environment in which the reactions mainly take place. Our results suggest that the H-transfer reaction of the BHT molecule with an alkoxy radical is spontaneous, and the energy barrier is small (∼0.1 eV) with a peroxy radical. Conversely, direct scavenging of alkyl radicals by BHT seems excluded. Our molecular dynamics simulations highlight the influence of steric hindrance and antioxidant diffusion within amorphous regions on antioxidant efficiency.

Generalized representative structures for atomistic systems.

A new method is presented to generate atomic structures that reproduce the essential characteristics of arbitrary material systems, phases, or ensembles. Previous methods allow one to reproduce the essential characteristics (e.g., the chemical disorder) of a large random alloy within a small crystal structure. The ability to generate small representations of random alloys, along with the restriction to crystal systems, results from using the fixed-lattice cluster correlations to describe structural characteristics. A more general description of the structural characteristics of atomic systems is obtained using complete sets of atomic environment descriptors. These are used within for generating representative atomic structures without restriction to fixed lattices. A general data-driven approach is provided here utilizing the atomic cluster expansion (ACE) basis. TheN-body ACE descriptors are a complete set of atomic environment descriptors that span both chemical and spatial degrees of freedom and are used within for describing atomic structures. The generalized representative structure (GRS) method presented within generates small atomic structures that reproduce ACE descriptor distributions corresponding to arbitrary structural and chemical complexity. It is shown that systematically improvable representations of crystalline systems on fixed parent lattices, amorphous materials, liquids, and ensembles of atomic structures may be produced efficiently through optimization algorithms. With the GRS method, we highlight reduced representations of atomistic machine-learning training datasets that contain similar amounts of information and small 40-72 atom representations of liquid phases. The ability to use GRS methodology as a driver for informed novel structure generation is also demonstrated. The advantages over other data-driven methods and state-of-the-art methods restricted to high-symmetry systems are highlighted.

Subtle Modifications in Interface Configurations of Iron/Cobalt Phthalocyanine-Based Electrocatalysts Determine Molecular CO2 Reduction Activities.

Strain engineering has emerged as a powerful approach in steering material properties. However, the mechanism and potential limitations remain poorly understood. Here we report that subtle changes in molecular configurations can profoundly affect, conducively or adversely, the catalytic selectivity and product turnover frequencies (TOFs) of CO2 reduction reaction. Specifically, introducing molecular curvature in cobalt tetraaminophthalocyanine improves the multielectron reduction activity by favorable *CO hydrogenation, attaining methanol Faradaic efficiency up to 52 %. In stark contrast, strained iron phthalocyanine exacerbates *CO poisoning, leading to decreased TOFCO by >50 % at -0.5 VRHE and a rapid current decay. The uniform dispersion is crucial for optimizing electron transfer, while activity is distinctly sensitive to the local atomic environment around the active sites. Specifically, local strain either enhances binding to intermediates or poisons the catalytic sites. Our comprehensive analysis elucidates the intricate relationship between molecular structure and activities, offering insights into designing efficient heterogeneous molecular interfaces.

Advanced EXAFS analysis techniques applied to the L-edges of the lanthanide oxides.

The unique properties of the lanthanide (Ln) elements make them critical components of modern technologies, such as lasers, anti-corrosive films and catalysts. Thus, there is significant interest in establishing structure-property relationships for Ln-containing materials to advance these technologies. Extended X-ray absorption fine structure (EXAFS) is an excellent technique for this task considering its ability to determine the average local structure around the Ln atoms for both crystalline and amorphous materials. However, the limited availability of EXAFS reference spectra of the Ln oxides and challenges in the EXAFS analysis have hindered the application of this technique to these elements. The challenges include the limited k-range available for the analysis due to the superposition of L-edges on the EXAFS, multielectron excitations (MEEs) creating erroneous peaks in the EXAFS and the presence of inequivalent absorption sites. Herein, we removed MEEs to model the local atomic environment more accurately for light Ln oxides. Further, we investigated the use of cubic and non-cubic lattice expansion to minimize the fitting parameters needed and connect the fitting parameters to physically meaningful crystal parameters. The cubic expansion reduced the number of fitting parameters but resulted in a statistically worse fit. The non-cubic expansion resulted in a similar quality fit and showed non-isotropic expansion in the crystal lattice of Nd2O3. In total, the EXAFS spectra and the fits for the entire set of Ln oxides (excluding promethium) are included. The knowledge developed here can assist in the structural determination of a wide variety of Ln compounds and can further studies on their structure-property relationships.

Mechanism of magnetic phase transition in correlated magnetic metal: insight into itinerant ferromagnet Fe3−δGeTe2

Developing a comprehensive magnetic theory for correlated itinerant magnets poses challenges due to the difficulty in reconciling both local moments and itinerant electrons. In this work, we investigate the microscopic process of magnetic phase transition in ferromagnetic metal Fe3−δGeTe2. We find that Hund’s coupling is crucial for establishing ferromagnetic order. During the ferromagnetic transition, we observe the formation of quasiparticle flat bands and an opposing tendency in spectral weight transfer, primarily between the lower and upper Hubbard bands, across the two spin channels. Moreover, our results indicate that one of the inequivalent Fe sites exhibits Mott physics, while the other Fe site exhibits Hund’s physics, attributable to their distinct atomic environments. We suggest that ferromagnetic order reduces spin fluctuations and makes flat bands near the Fermi level more distinct. The hybridization between the distinctly flat bands and other itinerant bands offers a possible way to form heavy fermion behavior in ferromagnets. The complex interactions of competing orders drive correlated magnetic metals to a new frontier for discovering outstanding quantum states.

Engineering the Local Atomic Environments of Te-Modulated Fe Single-Atom Catalysts for High-Efficiency O2 Reduction.

Atomically dispersed metal-nitrogen-carbon materials (AD-MNCs) are considered the most promising non-precious catalysts for the oxygen reduction reaction (ORR), but it remains a major challenge for simultaneously achieving high intrinsic activity, fast mass transport, and effective utilization of the active sites within a single catalyst. Here, an AD-MNCs consisting of defect-rich Fe-N3 sites dispersed with axially coordinated Te atoms on porous carbon frameworks (Fe1Te1-900) is designed. The local charge densities and energy band structures of the neighboring Fe and Te atoms in FeN3-Te are rearranged to facilitate the catalytic conversion of the O-intermediates. Meanwhile, the negative shift of the d-band center in FeN3-Te reduces the energy barrier limit for effective desorption of the final OH* intermediate. In the electrochemical evaluation, Fe1Te1-900 presents a more positive onset potential and half-wave potentials of 1.03 and 0.89V versus the reversible hydrogen electrode, respectively. Furthermore, the liquid zinc-air batteries assembled with Fe1Te1-900 exhibited excellent performances compared to commercial Pt/C. This work opens up new ideas for the development of high-performance ORR electrocatalysts for applications in various energy conversion and storage technologies.

K-Means Clustering in Fingerprint-Based Configuration Selection for Fitting Interatomic Potentials.

In this study, we present a method for selecting an arbitrary number of distinct configurations from a larger data set by applying k-means clustering to atomistic configuration fingerprints based on the CrystalNN model and radial distribution function (RDF). This approach improves the accuracy of fitting classical molecular dynamics interatomic potentials to density functional theory (DFT) data for both energies and forces while requiring fewer configurations than random selection. We demonstrate this improvement by fitting an embedded-atom method (EAM) potential for titanium, using various configurational sizes from an initial set of 1800 configurations. The k-means clustering consistently achieves better precision and lower standard deviations for a smaller number of configurations than random selection. The results also suggest that only about 30 configurations are sufficient to obtain an EAM model that describes well the full set of 1800 configurations in terms of energies and forces. Additionally, t-distributed stochastic neighbor embedding (t-SNE) method was used to reduce the configuration fingerprints into 2D space, and it revealed an overlap between two configuration subsets with and without Ti vacancy, indicating similar atomic environments. This similarity is captured by k-means clustering but not by random selection. Furthermore, when the overlapping configurations with vacancies were excluded from the k-means algorithm and used only as a test set, their energy and force predictions showed similar precision to those when they were included. This indicates that the overlapping configurations in the 2D t-SNE space indeed imply potential information redundancy among the atomistic configurations.

Automatic Feature Selection for Atom-Centered Neural Network Potentials Using a Gradient Boosting Decision Algorithm.

Atom-centered neural network (ANN) potentials have shown high accuracy and computational efficiency in modeling atomic systems. A crucial step in developing reliable ANN potentials is the proper selection of atom-centered symmetry functions (ACSFs), also known as atomic features, to describe atomic environments. Inappropriate selection of ACSFs can lead to poor-quality ANN potentials. Here, we propose a gradient boosting decision tree (GBDT)-based framework for the automatic selection of optimal ACSFs. This framework takes uniformly distributed sets of ACSFs as input and evaluates their relative importance. The ACSFs with high average importance scores are selected and used to train an ANN potential. We applied this method to the Ge system, resulting in an ANN potential with root-mean-square errors (RMSE) of 10.2 meV/atom for energy and 84.8 meV/Å for force predictions, utilizing only 18 ACSFs to achieve a balance between accuracy and computational efficiency. The framework is validated using the grid searching method, demonstrating that ACSFs selected with our framework are in the optimal region. Furthermore, we also compared our method with commonly used feature selection algorithms. The results show that our algorithm outperforms the others in terms of effectiveness and accuracy. This study highlights the significance of the ACSF parameter effect on the ANN performance and presents a promising method for automatic ACSF selection, facilitating the development of machine learning potentials.

Machine learning potential for the Cu-W system

Combining the excellent thermal and electrical properties of Cu with the high abrasion resistance and thermal stability of W, Cu-W nanoparticle-reinforced metal matrix composites and nano-multilayers are finding applications as brazing fillers and shielding material for plasma and radiation. Due to the large lattice mismatch between fcc Cu and bcc W, these systems have complex interfaces that are beyond the scales suitable for methods, thus motivating the development of chemically accurate interatomic potentials. Here, a neural network potential (NNP) for Cu-W is developed within the Behler-Parrinello framework using a curated training dataset that captures metallurgically relevant local atomic environments. The Cu-W NNP accurately predicts (i) the metallurgical properties (elasticity, stacking faults, dislocations, thermodynamic behavior) in elemental Cu and W, (ii) energies and structures of Cu-W intermetallics and solid solutions, and (iii) a range of fcc Cu/bcc W interfaces, and exhibits physically reasonable behavior for solid W/liquid Cu systems. As will be demonstrated in forthcoming work, this near accurate NNP can be applied to understand complex phenomena involving interface-driven processes and properties in Cu-W composites. Published by the American Physical Society 2024

Energetics of vacancy clusters in TiVTaW low-activation high-entropy alloy investigated by ab initio calculations

High-entropy alloys (HEAs) have attracted attention as potential candidates for nuclear fusion reactor materials due to their superior mechanical properties and irradiation resistance. The TiVTaW low-activation HEA exhibits an especially large variation in the cohesive energies of the pure metals of its alloying elements, leading to the characteristic of energetically heterogeneous atomic environments that differ at each atomic site, a focal point of this study. To evaluate the influence of this site-to-site variation on vacancy cluster formation, in this study, density functional theory calculations were conducted on small vacancy clusters consisting of the nearest neighbor configurations in equimolar TiVTaW. The monovacancy formation energies in TiVTaW were found to span a significantly broader range compared to those in other HEAs. Moreover, the range of vacancy cluster formation energies in TiVTaW was observed to be nearly identical to the range of the vacancy cluster formation energies of its alloying elements in the pure metals. Our calculations also showed that clusters with five or fewer vacancies exhibit a lower mean binding energy compared to the pure metals of the alloying elements, indicating that vacancy clusters in TiVTaW are less stable than those in pure metals. Additionally, the distribution of cluster binding energies for sizes less than five-vacancy clusters was found to range from −1.60 to 3.12 eV, which includes unstable clusters with negative binding energies. These energetic characteristics arising from the heterogeneous atomic environments unique to HEAs can influence void nucleation under irradiation. Furthermore, based on a thermodynamic model, local maxima in the free energy change correlating with cluster size were observed during the nucleation of vacancy clusters, indicating the generation of metastable small vacancy clusters. These findings provide a new perspective on irradiation damage mechanisms in general HEAs.

Model for the Shape of the Relaxation Spectrum in Glass Formers.

Traditionally the broadness of the spectrum of the relaxation times observed in glass-forming materials has been rationalized by local heterogeneity, where a variety of atomistic environments leads to spectrum of single-exponential relaxation responses. However, the assumption of heterogeneity can break down when tested against the shape of the relaxation spectrum. An alternative homogeneous scenario assumes that the relaxation is inherently multiexponential. A recently developed switchback model [Medvedev, G. A. Phys. Rev. E 2023, 107 (3), 034122] naturally results in a multiexponential wedge-like spectrum that is consistent with the dielectric relaxation, light scattering, and the NMR data. As a particular case the switchback model allows for the spectrum to become single-exponential; under the heterogeneous scenario this would require the heterogeneities to completely vanish, which is hard to justify. Using data from photobleaching experiments and molecular dynamic simulations, it is shown that the relaxation spectrum may become single-exponential under large anisotropic deformation. This is interpreted as an argument in favor of the homogeneous scenario and specifically the switchback model for the relaxation of the glass formers.

Machine learning interatomic potential with DFT accuracy for general grain boundaries in α-Fe

To advance the development of high-strength polycrystalline metallic materials towards achieving carbon neutrality, it is essential to design materials in which the atomic level control of general grain boundaries (GGBs), which govern the material properties, is achieved. However, owing to the complex and diverse structures of GGBs, there have been no reports on interatomic potentials capable of reproducing them. This accuracy is essential for conducting molecular dynamics analyses to derive material design guidelines. In this study, we constructed a machine learning interatomic potential (MLIP) with density functional theory (DFT) accuracy to model the energy, atomic structure, and dynamics of arbitrary grain boundaries (GBs), including GGBs, in α-Fe. Specifically, we employed a training dataset comprising diverse atomic structures generated based on crystal space groups. The GGB accuracy was evaluated by directly comparing with DFT calculations performed on cells cut near GBs from nano-polycrystals, and extrapolation grades of the local atomic environment based on active learning methods for the entire nano-polycrystal. Furthermore, we analyzed the GB energy and atomic structure in α-Fe polycrystals through large-scale molecular dynamics analysis using the constructed MLIP. The average GB energy of α-Fe polycrystals calculated by the constructed MLIP is 1.57 J/m2, exhibiting good agreement with experimental predictions. Our findings demonstrate the methodology for constructing an MLIP capable of representing GGBs with high accuracy, thereby paving the way for materials design based on computational materials science for polycrystalline materials.

LociPARSE: A Locality-aware Invariant Point Attention Model for Scoring RNA 3D Structures.

A scoring function that can reliably assess the accuracy of a 3D RNA structural model in the absence of experimental structure is not only important for model evaluation and selection but also useful for scoring-guided conformational sampling. However, high-fidelity RNA scoring has proven to be difficult using conventional knowledge-based statistical potentials and currently available machine learning-based approaches. Here, we present lociPARSE, a locality-aware invariant point attention architecture for scoring RNA 3D structures. Unlike existing machine learning methods that estimate superposition-based root-mean-square deviation (RMSD), lociPARSE estimates Local Distance Difference Test (lDDT) scores capturing the accuracy of each nucleotide and its surrounding local atomic environment in a superposition-free manner, before aggregating information to predict global structural accuracy. Tested on multiple datasets including CASP15, lociPARSE significantly outperforms existing statistical potentials (rsRNASP, cgRNASP, DFIRE-RNA, and RASP) and machine learning methods (ARES and RNA3DCNN) across complementary assessment metrics. lociPARSE is freely available at https://github.com/Bhattacharya-Lab/lociPARSE.

Unconventional Hexagonal Close-Packed High-Entropy Alloy Surfaces Synergistically Accelerate Alkaline Hydrogen Evolution.

Accelerating the alkaline hydrogen evolution reaction (HER), which involves the slow cleavage of HO-H bonds and the adsorption/desorption of hydrogen (H*) and hydroxyl (OH*) intermediates, requires developing catalysts with optimal binding strengths for these intermediates. Here, the unconventional hexagonal close-packed (HCP) high-entropy alloy (HEA) atomic layers are prepared composed of five platinum-group metals to enhance the alkaline HER synergistically. The breakthrough is made by layer-by-layer heteroepitaxial deposition of subnanometer RuRhPdPtIr HEA layers on the HCP Ru seeds, despite the thermodynamic stability of Rh, Pd, Pt, and Ir in a face-centered cubic (FCC) structure except for Ru. The synchrotron X-ray absorption spectroscopy (XAS) confirms the atomic mixing and coordination environment of HCP RuRhPdPtIr HEA. Most importantly, they exhibit notable improvements in both electrocatalytic activity and durability for the HER in an alkaline environment, as compared to their FCC RuRhPdPtIr counterparts. Electrochemical measurements, operando XAS analysis, and density functional theory unveil that the binding strengths of H* and OH* intermediates on the active Pt and Ir sites can be weakened and strengthened to a moderate level, respectively, by mixing non-active Ru, Rh, and Pd atoms with Pt and Ir atoms within the HCP HEA with strong synergistic electronic effects.

Analysis of Local Structure of Mechanical and Thermal Rearrangements in Glasses with the Atomic Cluster Expansion.

We explore the structural signatures of excitations in amorphous materials with the atomic cluster expansion (ACE), a universal and complete linear basis of descriptors of the atomic environment. Body-orderd linear classifiers are constructed that distinguish between active and inactive particles in three different model glass formers, in which structural relaxation occurs either through spontaneous thermal activation or by simple shear. We find that in binary mixtures, maximum prediction accuracy is already achieved with very few two-body correlations, while a polymer glass requires both two- and three-body correlations. Trends are robust across both activation mechanisms.

A Multipole-Based Reactive Force Field for Hydrocarbons.

The computational complexity of quantum chemistry methods has prompted the development of reactive force fields, facilitating practical applications of molecular dynamics simulations for large-scale reactive systems. Current reactive force fields typically employ intricate corrections based on prior chemical knowledge, which severely impedes their further advancement. This study presents a new atomic multipole-based reactive model with bond free (OPERATOR). The force field is constructed on a simple, physically motivated model within the AMOEBA framework that closely resembles the physical representation of the chemical reaction processes. In the force field, the atomic multipoles are generated dynamically according to the atomic environments, aiming to effectively capture significant changes in the electrostatic environments during chemical reactions. Subsequently, atomic multipole-based charge penetration, polarization, and charge transfer effects are incorporated into the force field to describe the complex electrostatic interactions in the system. The force field also includes van der Waals interactions and three-body potentials. In addition, to extend these nonreactive interactions to chemical reactions, the atom distribution multipole moments are used to characterize different chemical environments. The force field has been optimized using the dataset of potential energy surfaces (PESs) of hydrocarbons derived from DFT results of millions of conformations with six degrees of freedom (DOFs). The results demonstrate that the new force field effectively replicates both the monopoles and the energies. In comparison to ReaxFF, the new force field exhibits comparable or superior performance. Furthermore, molecular dynamics simulations of n-heptane decomposition effectively reproduce the primary products and reactions observed in the experiments. Given the simplicity and physically motivated nature of the model, it is expected that the new force field will be utilized in future studies to investigate chemical reaction mechanisms involving more elements.