Atomic-scale Calculations Research Articles

VASE: A High-Entropy Alloy Short-Range Order Structural Descriptor for Machine Learning.

The short-range order (SRO) structure in high-entropy alloys (HEAs) is closely associated with many properties, which can be studied through density functional theory (DFT) calculations. Atomic-scale modeling and calculations require substantial computational resources, and machine learning can provide rapid estimations of DFT results. To describe SRO information in HEAs, a new descriptor based on Voronoi Analysis and Shannon Entropy (VASE) is proposed. Based on Voronoi analysis, the Shannon entropy is introduced to directly characterize atomic spatial arrangement information except for composition and atomic interactions, which is necessary for describing the disorder atomic occupancy in HEAs. The new descriptor is used for predicting the formation energy of FeCoNiAlTiCu system based on machine learning model, which is more accurate than other descriptors (Coulomb matrices, partial radial distribution functions, and Voronoi analysis). Moreover, the model trained based on VASE descriptors exhibits the best predictive performance for unrelaxed structures (24.06 meV/atom). The introduction of Shannon entropy provides an effective representation of atomic arrangement information in HEAs, which is a powerful tool for investigating the SRO phenomena.

Phase formation mechanism of Cr2AlC MAX phase coating: In-situ TEM characterization and atomic-scale calculations

Cr2AlC, a representative MAX phase, gains increasing attention for the excellent oxidation tolerance and corrosion resistance used in harsh high temperature and strong radiation environments. However, the lack of the phase formation mechanism has become the key bottleneck to the practical applications for Cr2AlC synthesis with high purity at low temperatures. In this work, we fabricated the amorphous Cr–Al–C coating by a hybrid magnetron sputtering/cathodic arc deposition technique, in which the in-situ heating transmission electron microscopy (TEM) was conducted in a temperature range of 25–650 °C to address the real-time phase transformation for Cr2AlC coating. The results demonstrated that increasing the temperature from 25 to 370 °C led to the structural transformation from amorphous Cr–Al–C to the crystalline Cr2Al interphases. However, the high-purity Cr2AlC MAX phase was distinctly formed at 500 °C, accompanied by the diminished amorphous feature. With the further increase of temperature to 650 °C, the decomposition of Cr2AlC to Cr7C3 impurities was observed. Similar phase evolution was also evidenced by the Ab-initio molecular dynamics calculations, where the bond energy of Cr–Cr, Cr–Al, and Cr–C played the key role in the formed crystalline stability during the heating process. The observations not only provide fundamental insight into the phase formation mechanism for high-purity Cr2AlC coatings but also offer a promising strategy to manipulate the advanced MAX phase materials with high tolerance to high-temperature oxidation and heavy ion radiations.

Impact toughness and fracture propagation mechanism of NiAl precipitation-strengthened HSLA steels

Impact toughness is essential for evaluating the mechanical properties of ship hull steels. This study focused on understanding the embrittlement mechanism of NiAl precipitation-strengthened HSLA steels by integrating advanced characterization and atomic-scale calculations. The factors causing the deterioration of the impact toughness of NiAl precipitation-strengthened steels, which have been contentious, were identified. The embrittlement mechanism and crack propagation mode were revealed using first-principles calculations and 3D impact fracture morphology, respectively. The results suggested that the numerous homogeneous NiAl nanoparticles within the bcc-Fe matrix reduced the impact toughness of the HSLA steels. As the precipitate interparticle spacing (L) decreased, the impact toughness decreased until it attained a critical value (∼27 nm). This is interpreted effectively by the calculations indicating that the shear modulus (75 GPa) and fracture energy (4.5 J/m2) of the NiAl phase, particularly the NiAlMn phase (46 GPa, 4 J/m2), are significantly lower than those of bcc-Fe (83 GPa, 5 J/m2). This induces the fracture of nanoparticles under rapid impact loading, which functions as numerous crack initiations before plastic deformation of the matrix. The small L achieved after peak-hardening aging can result in the interconnection of these crack sources and cause instantaneous cleavage fractures, similar to brittle materials.

The role of atomistic processes in growth of Cu–Ni metallic/bimetallic nanoparticles

Controlling the morphology of non-noble bimetallic nanocrystals can provide an excellent opportunity to improve performance and activity in catalytic reactions. Although several studies have focused on the overall macroscopic description of the synthesis process, identifying the leading factors in a typical crystal growth process at the atomic scale is still challenging. Here we report the results of atomic scale calculations on the shape evolution of bimetallic Cu–Ni nanoparticle growth using molecular static and dynamic simulations. Our calculations show that statistical analysis of space and time characteristics of single atom diffusion mechanisms and their energy barriers provide sound guidance for fabricating end products with specific shapes and architecture in a growth process.

Tuning Elastic Properties of Metallic Nanoparticles by Shape Controlling: From Atomistic to Continuous Models.

Understanding and mastering the mechanical properties of metallic nanoparticles is crucial for their use in a wide range of applications. In this context, atomic-scale (molecular dynamics) and continuous (finite elements) calculations is used to investigate in details gold nanoparticles under deformation. By combining these two approaches, it is shown that the elastic properties of such nano-objects are driven by their size but, above all, by their shape. This outcome is achieved by introducing a descriptor in the analysis of the results enabling to distinguish among the different nanoparticle shapes studied in the present work. In addition, other transition-metal nanoparticles are considered (copper and platinum) using the aforementioned approach. The same strong dependence of the elastic properties with the shape is revealed, thus highlighting the universal character of the achievements.

Modelling of zirconium growth under irradiation and annealing conditions

Modelling zirconium growth under irradiation is a difficult task due to the different types of defects formed which all contribute to the macroscopic deformation. An Object Kinetic Monte Carlo model parameterized using state-of-the-art atomic-scale calculations and experimental data is presented. It is able to reproduce a good macroscopic deformation, the coexistence of all types of defects: 〈a〉 interstitial and vacancy loops, 〈c〉 vacancy loops and the alignments of 〈a〉 loops parallel to the basal plane. A large parametric study is performed in order to understand thoroughly in which conditions these microstructural features can be observed. The model is then applied to study flux effects and the microstructure evolution during annealing. It can be concluded that 〈a〉 loop alignments are phenomena which are very sensitive to the flux. During annealing, the defects can recover in a non-monotonous way and 〈a〉 loops inside the alignments disappear at last, which illustrates their strong stability.

Models of configurationally-complex alloys made simple

We present a Python package for the efficient generation of special quasi-random structures (SQS) for atomic-scale calculations of disordered systems. Both, a Monte-Carlo approach or a systematic enumeration of structures can be used to carry out optimizations to ensure the best optimal configuration is found for given cell size and composition. We present a measure of randomness based on Warren-Cowley short-range order parameters allowing for fast analysis of atomic structures. Hence, optimal structures are found in a reasonable time for several dozens or even hundreds of atoms. Both SQS optimizations and analysis of structures can be carried out via a command-line interface or a Python API. Additional features, such as optimization towards partial ordering or independent sublattices allow the generation of atomistic models of modern complex materials. Moreover, hybrid parallelization, as well as distribution of vacancies, are supported. The output data format is compatible with ase, pymatgen and pyiron packages to be easily embeddable in complex simulation workflows. Program summaryProgram title: sqsgeneratorCPC Library link to program files:https://doi.org/10.17632/m2sb3wzcvc.1Developer's repository link:https://github.com/dgehringer/sqsgeneratorLicensing provisions: MITProgramming language: Python, C++Supplementary material:https://sqsgenerator.readthedocs.ioNature of problem: Many technological relevant materials, exhibit a crystalline disorder. Within atomistic modelling approaches such as Density Functional Theory (DFT) or Molecular Dynamics, disorder is modelled with a cell containing a (small) finite set of atoms. Such an atomic configuration is usually found by enumerating structures. However, since configurational space is growing exponentially efficient tools are needed to sample it properly.Solution method: Efficient quantification of disorder using a generalization of Warren-Cowley Short Range Order (WC-SRO) parameters [1,2]. By either a Monte-Carlo approach or systematic enumeration, optimal structures can be found. The software is distributed as a Python package offering a command line interface. Core parts are written in C++ and exhibit shared (OpenMP) and distributed (MPI) memory parallelism. For embedding into complex simulation workflows, the tool exposes a Python API to integrate into popular packages such as ase [3], pymatgen [4] or pyiron [5].

Prediction of the evolution of defects induced by the heated implantation process: Contribution of kinetic Monte Carlo in a multi-scale modeling framework

Extended defects formed as a result of heated implantation and thermal annealing are studied using transmission electron microscopy and kinetic Monte Carlo simulations. We highlight the relevance of using kinetic Monte Carlo approach to provide information to continuum-scale simulations as well as the value of integrating data from atomic-scale calculations for its calibration.

Electrochemical behavior and interfacial bonding mechanism of new synthesized carbocyclic inhibitor for exceptional corrosion resistance of steel alloy: DFTB, MD and experimental approaches

A new carbocyclic compound, namely 3-benzoyl-4-hydroxy-4-phenyl-2,6-(4-methylphenyl)cyclohexane-1,1-dicarbonitrile (MPC) was synthesized and characterized. Herein, MPC was used as green compounds and its anti-corrosion performance was evaluated on the basis of singular role of electron donor–acceptor of MPC molecule. For this purpose, a combination of experimental studies and electronic-/atomic-scale calculations were performed in a bid to understand the electrochemical behavior and interfacial mechanism of MPC molecule based on the correlation between electron charge transfer and adsorption mechanism. Theoretical perspectives are also used to validate the significant inhibition feature achieved by the experimental studies and propose a mechanism of adsorption by using density functional theory (DFT) and molecular dynamic (MD) simulations. According to DFT and MD perspectives, it is found that MPC presents strong interaction with metal surface due to its considerable ability to provide lone pair electrons for electrophilic attacks. This is demonstrated by the high adsorption energy (-5.83 eV) and the parallel configuration of MPC which reveal the formation of molecular self-assembly triggered by an organic-surface interaction. The reliable corrosion stability was provided for 72 h of immersion at an optimum concentration with a fairly high inhibition efficiency (85.81 %) due to the formation of organic inhibitive layer. The addition of MPC inhibitor worked as a sealing agent to reduce the corrosion rate, thus forming a dense and protective barrier on the metal surface. The corrosion resistance of mild steel sample was enhanced significantly due to a high adsorption ability arising from the electron-rich nature of molecule. The formation of organic layer on the metal surface was discussed in relation to the intermolecular interactions and microstructural observations by considering the charge transfer behavior responsible for exceptional corrosion protection of steel alloys. The computational simulations were consistent with the experimental results and confirm the importance of developing eco-friendly hybrid materials.

Critical issues on coherent interface energy calculations revisited: The case of Al/TiB[formula omitted

Properties of Al/TiB2 heterophase interfaces are investigated by means of atomic-scale calculations. Focusing on practically important (111)Al//(0001)TiB2basal interfaces, our study allows to clarify various ambiguities present in the literature when calculating coherent interface energies in non-binary systems: (i) while neglected in earlier works on Al/TiB2, elasticity effects are properly taken into account, (ii) a critical point determining interface stability being related to chemical potentials in ordered compounds, their ranges of values are determined by a careful analysis ensuring TiB2 stability and absence of undesired other phases, and (iii) comparing different simulation systems leads to conclude that periodic boundary conditions should be preferred to free surface ones, frequently used in earlier studies. This work is the first attempt to bring the improvements (i) to (iii) in the same methodology and allows to obtain more realistic values of coherent interface energies than those previously available in the literature.

Probing the structural evolution and its impact on magnetic properties of FeCoNi(AlMn)x high-entropy alloy at the nanoscale

We report the first nanoscale investigation of FeCoNi(AlMn) x high-entropy alloys (HEAs) processed by laser metal deposition. The structural evolution of the alloy upon chemical composition variation (0.2 ≤ x ≤ 1.5) was investigated by combining imaging and spectroscopies in (scanning) transmission electron microscopy (S)TEM with density functional theory (DFT). A gradual change from a face-centered cubic (FCC) towards an ordered full-Heusler (L2 1 ) phase by increasing the Al and Mn contents was observed. Direct imaging and atomic-scale calculations revealed a nanoscale interplay between B2 and L2 1 ordered structures for x = 1.5, wherein the latter, Al and Mn occupy two different Wyckoff sites. By decreasing x, the FCC phase dominates exhibiting intense phase separation tendency, ordering phenomena, and nano-precipitation. Although not chemically discriminated, plasmon-peak splitting in low-loss electron energy loss spectra revealed the presence of two valence electron densities within the FCC phase. Lorentz TEM showed that the ordered nano-precipitates and nano-sized grains with a structure based on a tripled FCC unit cell are pinning-sites for magnetic domain walls and dislocations. All alloy compositions exhibited soft-magnetic behavior with coercivity ( H c ) values< 1000 A/m. The FeCoNi(AlMn) 1.5 alloy with L2 1 /B2 nanostructure showed the highest magnetization ( M s ) with relatively low H c , attributed to the large magnetic moment of Mn and the synergistic effect of Mn-Al according to DFT, whilst ordering does not impose a negative effect. Phase separation trends within the FCC phase seem to decrease the M s however, the overall impact on the magnetic behavior is not intense, opening up for new avenues for tuning FeCoNiAlMn properties through chemically-designed phase decomposition regimes. • First nanoscale investigation of FeCoNi(AlMn)x HEAs processed by LMD, combining advanced (S)TEM and DFT calculations. • Ordering phenomena - nanoscale interplay between B2 and L21, ordered superstructures - and impact on magnetic properties. • Atomic site occupancy resolved by direct imaging and atomic-scale calculations. • Phase separation in FCC opens up avenues for tuning alloy properties through chemically-designed decomposition regimes. • All alloy compositions exhibited soft-magnetic behavior with coercivity values< 1000 A/m.

Efficiency of the vacancy pipe diffusion along an edge dislocation in MgO

This study focuses on the mechanisms of pipe diffusion and the kinetic of point defect diffusion along dislocation line in MgO. We developed a numerical approach, based on atomic scale calculations and the use of the elasticity theory, to determine the migration energies of point defects. The kinetic of diffusion along the dislocation is then evaluated according to a on-lattice atomistic kinetic Monte Carlo algorithm informed by atomistic simulations. We show that edge dislocation in MgO behaves as a strong sink for vacancies which, combined with a lower migration energy at dislocation core region, strongly enhances the diffusion of point defect in the vicinity of the dislocation with respect to the bulk material. At low and intermediate temperatures, pipe diffusion results in an increase of the diffusivity of several order of magnitude. Accounting more precisely for the effect of pipe diffusion may therefore be a key to reconcile the experimentally measured scattering of diffusivity in MgO.

Cover Feature: Strong Structural and Electronic Binding of Bovine Serum Albumin to ZnO via Specific Amino Acid Residues and Zinc Atoms (ChemPhysChem 2/2022)

The Cover Feature illustrates how bovine serum albumin binds to polar and non-polar ZnO single-crystal facets via its specific amino acids. Atomic scale calculations using density-functional tight-binding models are shown in the circles for specific binding cases, depending also on ZnO facet orientations. More information can be found in the Article by Hadi Hematian and co-workers.

First-principles modeling of complexions at the phase boundaries in Ti-doped WC-Co cemented carbides at finite temperatures

WC-Co cemented carbides have a unique combination of high hardness and good toughness, making them ideal as tool materials in applications such as metal machining or rock drilling. Dopants are commonly added to retard grain growth and thereby creating a harder material. Thin films with cubic structure have been observed experimentally at phase boundaries between hexagonal WC and fcc Co-rich binder when doping with, e.g., Ti, V, or Cr. These films are generally considered to play a crucial role in the grain growth inhibition effect. Therefore, the thermodynamics of these thin cubic films is important to understand. Here, we construct, using ab initio calculations and modeling, an interfacial phase diagram for thin cubic films in Ti-doped WC-Co. We consider $\mathrm{C}\ensuremath{\leftrightarrow}\mathrm{vacancy}$ and $\mathrm{W}\ensuremath{\leftrightarrow}\mathrm{Ti}$ substitutions by constructing alloy cluster expansions and use Monte Carlo simulations to calculate the configurational free energy. Furthermore, force-constant fitting is used to extract the harmonic free energy for the ground-state structures. Additionally, we use information from thermodynamic databases to couple our atomic-scale calculations to overall compositions of typical WC-Co materials. We predict that Ti segregates to WC/Co phase boundaries to form thin cubic films of two metallic layer thickness, both at solid-state and liquid-phase sintering temperatures. Furthermore, we predict that these films are stable also for low doping concentrations when no Ti-containing carbide phase precipitates in the material. We show that Ti essentially only segregates to the inner layer of the thin cubic film leaving an almost pure W layer towards Co, an ordering which has been observed in recent experimental high-resolution transmission electron microscopy studies.

Computational catalyst discovery: Active classification through myopic multiscale sampling.

The recent boom in computational chemistry has enabled several projects aimed at discovering useful materials or catalysts. We acknowledge and address two recurring issues in the field of computational catalyst discovery. First, calculating macro-scale catalyst properties is not straightforward when using ensembles of atomic-scale calculations [e.g., density functional theory (DFT)]. We attempt to address this issue by creating a multi-scale model that estimates bulk catalyst activity using adsorption energy predictions from both DFT and machine learning models. The second issue is that many catalyst discovery efforts seek to optimize catalyst properties, but optimization is an inherently exploitative objective that is in tension with the explorative nature of early-stage discovery projects. In other words, why invest so much time finding a "best" catalyst when it is likely to fail for some other, unforeseen problem? We address this issue by relaxing the catalyst discovery goal into a classification problem: "What is the set of catalysts that is worth testing experimentally?" Here, we present a catalyst discovery method called myopic multiscale sampling, which combines multiscale modeling with automated selection of DFT calculations. It is an active classification strategy that seeks to classify catalysts as "worth investigating" or "not worth investigating" experimentally. Our results show an ∼7-16 times speedup in catalyst classification relative to random sampling. These results were based on offline simulations of our algorithm on two different datasets: a larger, synthesized dataset and a smaller, real dataset.

(Invited) Atomistic Understanding on the Surface of GaAs By Ab Initio Thermodynamics; From Equilibrium Shape to Growth Shape

The III-V semiconductor nanowires (NWs) have attracted substantial attention as an ideal system for the study on the growth mechanism and electronic device applications. For the precise manipulation of the nanowire growth, understanding on the temperature–pressure (T–P) dependent variation in surface energy and reconstruction which governs the anisotropic interaction between vapor sources and crystal solids is crucial. Unfortunately, the quantitative values of the variation in surface energy cannot be obtained only using the conventional electronic structure calculation. To overcome this difficulty, we have developed a reliable ab initio thermodynamic method by which the T–P dependent surface energy is predicted from the atomic-scale calculations on: surface reconstructions of InAs1; equilibrium crystal shapes of GaAs and InAs2; and anisotropic adsorption and growth conditions of GaAs NW3.In this talk, we present an ab initio thermodynamics approach by combining the atomic-scale calculation with the thermodynamic treatment to take a step forward in the theoretical modeling on the growth of polar GaAs <111> nanowire by calculating the change in Gibbs free energy during the nucleation and growth process. By comparing the nucleation rate between different stacking sequences, the formation probability of stacking fault or wurtzite segment was quantitatively predicted under arbitrary vapor-phase growth conditions. The predicted T–P dependent probability from atomic-scale calculation is directly comparable to the experiments and remarkably consistent with the available results to date, elucidating the fundamental asymmetric growth mechanism. Our ab initio approach is truly independent of any empirical data, bridging to the experimentally controllable variables. This simulation can be applied to various nanomaterials, including two-dimensional materials as well as other III-V and II-VI NWs, where the precise control of the stacking sequence is significant for their properties.[1] Yeu et al., Sci. Rep., 2017, 7, 10691[2] Yeu et al., Sci. Rep., 2019, 9, 1127[3] Yeu et al., Appl. Surf., Sci., 2019, 497, 143740

Atomic-scale origin of ultrahigh piezoelectricity in samarium-doped PMN-PT ceramics

Designing high-performance piezoelectric materials based on atomic-scale calculations is highly desired in recent years, following the understanding of the structure-property relationship of state-of-the-art piezoelectric materials. Previous mesoscale simulations showed that local structural heterogeneity plays an important role in the piezoelectric property of ferroelectrics; that is, larger structural heterogeneity leads to higher piezoelectricity. In this Rapid Communication, by combining first-principles calculations and experimental characterizations, we explored the atomic-scale origin of the high piezoelectricity for samarium-doped $\mathrm{Pb}(\mathrm{M}{\mathrm{g}}_{1/3}\mathrm{N}{\mathrm{b}}_{2/3}){\mathrm{O}}_{3}\text{\ensuremath{-}}\mathrm{PbTi}{\mathrm{O}}_{3}$ (PMN-PT) ceramics, which possesses the highest piezoelectric ${d}_{33}$ of $\ensuremath{\sim}1500\phantom{\rule{0.16em}{0ex}}\mathrm{pC}\phantom{\rule{0.28em}{0ex}}{\mathrm{N}}^{\ensuremath{-}1}$ among all known piezoelectric ceramics. The impacts of various dopants on local structure and piezoelectric properties of PMN-PT ceramics were investigated in terms of the effective ionic radius and cation valence. Our results show that A-site dopants with a valence of 3+ are more effective to produce local structural heterogeneity in PMN-PT when compared with the A-site dopants with a valence of 2+, and a smaller dopant size leads to a larger variation of local structure. According to this study, the outstanding piezoelectricity in Sm-doped PMN-PT ceramics is attributed to the fact that $\mathrm{S}{\mathrm{m}}^{3+}$ is the smallest ions that can entirely go to the A site of PMN-PT rather than the B site. The present work may benefit the design of high-performance piezoelectric materials based on the concept of local structural engineering.

Quantitative insights into the growth mechanisms of nanopores in hexagonal boron nitride

The formation of nanopores under electron irradiation is an ideal process to quantify chemical bonds in two-dimensional materials. Nowadays, high-resolution transmission electron microscopy (HRTEM) allows investigating such nucleation and growth phenomena with incomparable spatial and temporal resolution. Moreover, theoretical calculations are usually exploited to confirm characteristic features of these atomic-scale observations. Nevertheless, the full understanding of the ejection mechanisms of atoms requires a detailed investigation of the interplay between the very dynamic edge structure of expanding nanopores and the displacement energy of edge atoms $({E}_{D})$. Here, the dynamics of triangular nanopores in hexagonal boron nitride (h-BN) under various electron dose rates was followed by aberration-corrected HRTEM with high temporal resolution to provide new in situ insights into their growth processes. We reveal that the ejection of atomic pairs is an elemental mechanism that considerably speeds up the expansion of nanopores. Atomic-scale calculations were exploited to quantify the structure-dependent ${E}_{D}$ of all the ejected edge atoms. They revealed strong variations of this threshold energy during the growth processes. This quantitative study reconciles theoretical and experimental measurements of the ejection rate of atoms in h-BN under electron irradiation, which is essential for nanopore engineering in this atomically thin membrane.

A phase field model for dislocations in hexagonal close packed crystals

In this work, an application of a phase field formulation suitable for modeling the motion of individual partial and full dislocations in hexagonal close packed (HCP) crystals is presented. The formulation incorporates periodic potentials for glide on the distinct HCP slip systems, which are informed here by density functional theory (DFT). The model is applied to simulate the dissociation process starting from an unstable perfect dislocation and ending at its final equilibrium structure for different slip planes and in different HCP metals. The structural characteristics that are predicted for these dislocations include the partial Burgers vectors, dissociation distances, core widths of the partials, and any asymmetries in these quantities. Mg is selected as one of the model materials since its dislocations are the most well studied and it is nearly elastically isotropic. For Mg, it is shown that the predictions for dissociation distances agree with those reported previously by atomic-scale calculations, including density functional theory, for dislocations on the basal < a > , prismatic < a > , and pyramidal type II <c+a> slip systems. Phase field model results are also presented for dislocations in Ti and Zr, which we find develop distinctively different equilibrium structures than Mg.

Ab initio prediction of strong interfacial bonding in the Fe|Al bimetallic composite system

Atomic-scale calculations were used to investigate the formation of interfacial bonds in the Fe|Al bimetallic composite system. The results indicate that chemical bonds at pristine Fe|Al interface display more covalent character than constituent materials resulting in enhanced interface strength. Such an interface exhibits large cohesive energy exceeding significantly the value of bulk aluminum. The effect of two technologically relevant solute elements, i.e. carbon and magnesium on the interface strength was analyzed. Our robust yet simple computational model can be used to predict the most efficient combination of steels and aluminum alloys for cold pressure welding, leading to strong interfacial bonding.