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In the advent of the digital revolution, Artificial Intelligence (AI) has emerged as a pivotal tool in various domains, including materials design and discovery. This paper provides a comprehensive review of the AI methodologies integrated within this context, encompassing materials informatics, density functional theory, molecular dynamics, and finite elements analysis. We further delve into the transformative role of AI within process engineering, manufacturing, and industry 4.0, with a focus on manufacturing process optimization techniques. Highlighting the importance of active learning, self-correcting processing, and digital twins in the era of smart manufacturing, this review underscores the impact of big data and data quality. The paper provides an insight into the challenges and future prospects, pointing towards the tremendous potential AI holds for revolutionizing the field of materials science. • Comprehensive exploration of AI methods in Materials Design and Discovery. • Review of the transformative role of AI within process engineering. • Discussion on the importance of data quality and quantity in the era of Big Data. • Insight into challenges and potential of AI in the field of Materials Science. [ABSTRACT FROM AUTHOR]

[Display omitted] • For the Cu 7 M, Cu 8 M, and Cu 9 M atomic clusters, the M metal atoms tend to be centered. • We found that the addition of transition metals enhanced the stability of clusters. • The addition of transition metals increased the adsorption energy of CO 2 clusters. • In the reaction of catalytic CO 2 reduction, CunNi was found to be the preferred catalyst. The efficient and selective electroreduction of CO 2 in chemical fuels can be environment-energy friendly; however, it highly relies on catalysts with controllable selectivity and reaction paths. At present, copper-based catalysts are a good option for CO 2 reduction, and the formation of C 1 + products is presented in this paper. First, we systematically studied the three-dimensional structures of the Cu 4-12 M clusters (a total of nine types). The M atoms were sequentially replaced by first-row transition metals (Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn), which enhanced the stability of the clusters and their CO 2 adsorption energy. Second, studies on the catalytic reduction of CO 2 indicated that these clusters tend to produce methane through pathways mediated by CHO*. Finally, we summarized the lowest rate-determining steps of all clusters and identified the most suitable catalyst candidates. This work advances the theoretical study of nanoclusters for catalytic CO 2 reduction. [ABSTRACT FROM AUTHOR]

[Display omitted] • The machine-learned potential for aluminum was developed as based on Behler-Parrinello neural network scheme. • The potential demonstrates an excellent agreement with experimental data and DFT calculations concerning thermodynamic, elastic, liquid phase and generalized stacking fault energy properties. • High accuracy was achieved due to the optimal training set and configuration parameters used to generate the machine learning potential. Aluminum and its alloys are most often used as structural materials due to their specific properties, such as low weight, low energy consumption for remelting and the possibility of almost complete processing. This paper utilizes machine learning, specifically the Behler-Parrinello neural network scheme, to develop a powerful potential for studying the underlying mechanisms of deformation, fracture, and defect formation. By surpassing the limitations of first principles calculations, the application of machine-learned potentials (MLP) becomes highly advantageous for describing pure aluminum (Al) in its solid and liquid phases. Specifically, from the generated potential equilibrium, thermodynamic, elastic, and vibrational properties of face-centered cubic (fcc) Al are obtained in very good agreement especially with density functional theory (DFT) results as well as with previous calculations using existing semi-empirical potentials, such as EAM and MEAM, recent machine-learned potentials, and experimental data. Furthermore, our potential proves to accurately reproduce defect formation energies such as previously computed and measured stacking-fault energy curves. Finally, stacking fault profiles as well as key quantities of the liquid phase such as the melting point at ambient pressure, temperature-dependent densities, and radial distribution functions are also calculated in very good agreement with the results from previous theoretical and experimental investigations. Nevertheless, our investigation goes beyond previous studies in proving excellent agreement with experimental data especially of the specific heat and the melting point at very high pressures. The competitive analysis performed in this work thus clearly demonstrates the validity and accuracy of the generated machine-learned potential to describe a wide range of properties of Al at various temperatures and pressures and thereby lays ground for future applications. [ABSTRACT FROM AUTHOR]

In the density functional theory (DFT), it is well-known that the generalized gradient approximation (GGA) underestimates the energy gap. One of the commonly used GGA functionals is the Perdew–Burke–Ernzerhof (PBE) functional. This paper presents a machine learning approach to correct the energy gap estimated by PBE, using a minimal set of five features, to the accuracy level of the many-body perturbation theory G 0 W 0 band gap, which is known for its precision but high computational cost. These features are identified by seeking those which can be obtained directly from PBE-DFT calculations or from the standard atomic tables and directly connected to Coulombic interaction, and by minimizing feature correlations. The utilized features are the PBE band gap and the average atomic distance (obtained directly from PBE-DFT calculations) and the average oxidation states, electronegativity, and the minimum difference of electronegativity between constituents (obtained from the standard atomic tables). These features do not require any further atomistic calculations. The developed models are trained and validated using 265 inorganic semiconductors and insulators with energy gaps ranging from 0.75 to 14.55 eV. Several regression techniques have been applied, all of which have produced highly accurate and comparable results. This suggests that the five features employed in the analysis effectively capture the underlying physics. The most accurate model is based on Gaussian process regression (GPR), achieving a validation accuracy of 0.252 eV root-mean-square error (RMSE) and an R 2 value of 0.9932. The best bootstrapped model achieves an RMSE of 0.232 eV. The developed GPR model is then applied successfully to completely independent data sets. The developed model should allow calculating gaps with G 0 W 0 accuracy using the relatively much faster DFT-PBE. This is especially useful in materials discovery where computational efficiency is crucial. Furthermore, the model should allow empirical investigation of the influences of different compositional and structural characteristics on the gap. • Bandgap underestimation problem in DFT-PBE is corrected using machine learning with a minimal set of five features. • A Gaussian process regression achieved a validation RMSE of 0.252 eV and an R 2 of 0.9932. • Features are obtained directly from PBE-DFT calculations or from standard atomic tables. [ABSTRACT FROM AUTHOR]

[Display omitted] In this paper, the characteristics of oxygen vacancies (V O) and gallium vacancies (V Ga) in β-Ga 2 O 3 are studied using density functional theory. The performed relaxation analysis indicates that the relaxation of V GaⅡ increases its octahedral volume. Compared with the tetrahedron, the Ga atom of the octahedron surrounding V O is more stable. The formation energy results suggest also that V Ga 0 is expected to be most favorable in p-type β-Ga 2 O 3 , while V Ga -3 is preferred in the n -type. The −3 charge state acts as a compensating acceptor, which reduces electron conductivity. V O +2, which acts as the compensation donor center, can easily form in p-type β-Ga 2 O 3 , while the V O 0 forms readily in n -type β-Ga 2 O 3. V Ga at high density can exist in β-Ga 2 O 3 in an oxygen-rich environment, while V O can exist under oxygen-poor conditions. The defect charge-state transition level reveals that V Ga is a deep acceptor and V O is a deep donor. For V Ga , the transition levels ε (0/-1), ε (-2/-1), and ε (-3/-2) can appear in the thermodynamic equilibrium system. However, for V OⅠ and V OIII , which are used as negative-U centers, only the transition level ε (2+/0) can appear in thermodynamic equilibrium. Furthermore, the calculated bandgap properties indicate that V Ga is capable of introducing ferromagnetism in β-Ga 2 O 3 , while V O cannot. V Ga introduces both acceptor levels and donor levels within the bandgap, which compensate each other, and they, thus, determine the acceptor characteristics of the system. The appearance of V O -introduced donor levels into the bandgap increases the overall electron concentration of the system. A Bader charge analysis shows that the vacancy defect can have a strong effect on the charge distribution of the crystal and alter the electronic characteristics of the crystal. The research results in this paper show that the electronic and magnetic properties of β-Ga 2 O 3 can be changed significantly by introducing different types of vacancy defects with different charge states during the preparation process of the device. This can be done to meet specific performance requirements of a commercial electronic component. [ABSTRACT FROM AUTHOR]

[Display omitted] • The dissociation of nitrogen molecules can be catalyzed on the Nb(1 1 0) surface. • The nitrogen atoms on the industrially heated niobium cavity are mostly accumulated on the surface and the subsurface. • Nitrogen can act as a "protective film" for niobium products, preventing them from being oxidized and protecting their superconducting properties from loss. High-temperature nitriding has been shown to improve the quality factor (Q) of the superconducting radio frequency (SRF) cavity recently, while its inherent promoting mechanism is still unclear. Therefore, based on first-principles calculations, the adsorption and dissociation processes of nitrogen molecules on the Nb(1 1 0) surface, and the adsorption, diffusion processes of nitrogen atoms on the Nb(1 1 0) surface, were studied in this paper to explain the principle of its performance improvement. To establish the reaction mechanism, adsorption preference sites were determined. All possible reaction paths were investigated for each step including the energy barrier based on the climbing image nudged elastic band (CI-NEB) method. We establish multiple reaction paths and the only optimal one was found. It is found that the Nb(1 1 0) surface can facilitate the dissociation of nitrogen molecules (with only a 1.14 eV energy barrier), and nitrogen atomic energy can readily reach the subsurface (only a 1.73 eV energy barrier). Still, the high energy barrier (up to 6.64 eV) prevents it from diffusing inward. This revelation provides the nitriding mechanism of pure niobium and the rationale for enhancing niobium cavity performance after nitrogen mixing. [ABSTRACT FROM AUTHOR]

[Display omitted] • Interatomic potential for AlN was developed based on deep learning method. • Deep potential predicts mechanical and thermal properties with quantum-level accuracy. • Deep potential predicts defect properties with quantum-level accuracy. • DP predicted thermal conductivity is in high agreement with experimental values. Due to its exceptional physical properties, such as high thermal conductivity and mechanical strength, AlN has been widely used in high-power, high-temperature electronic, and optoelectronic devices. Molecular dynamics simulation is a powerful tool to study its thermal and defect properties. The selection of interatomic potentials plays an important role in the accuracy of calculation results. However, molecular dynamics simulations with various interatomic potentials have yielded different results when investigating the thermal and defect properties of AlN over the last few decades. In this paper, an interatomic potential (DP-IAP) model is developed using a deep potential (DP) methodology for AlN, with the training model's datasets derived from density functional theory (DFT) calculations. The DP-IAP demonstrates quantum-level accuracy in the calculation of the mechanical properties, thermal transport properties, and the defects formation and defects migration for AlN. The developed DP model paves the way for modeling thermal transport and defect evolution in AlN-based devices. [ABSTRACT FROM AUTHOR]

[Display omitted] • Doped Rh can improve the conductivity of CeO 2. • Doped Rh enhanced the adsorption performance of CeO 2 to CO. • Better sensitivity, desorption time, and selectivity to CO. Dissolved gas analysis (DGA) is an effective method for fault detection of oil-immersed transformers, and CO is one of the characteristic gases in insulation oil. Therefore, based on density functional theory, the adsorption properties of three noble metals (Ag, Rh, or Au)-doped CeO 2 (1 1 0) to CO were studied in this paper. The adsorption energy, charge transfer, density of states (DOS), and deformation charge density (DCD) were calculated to analyze the adsorption behavior of CO on the material surface. The frontier molecular orbital, energy band structure, and desorption time were also calculated to evaluate the potential of the three doping systems as CO sensors. The results show that the adsorption properties of Ag-CeO 2 , Rh-CeO 2 , and Au-CeO 2 to CO are significantly higher than that of pristine CeO 2 , with adsorption energies are −54.61, −85.78, −205.57 kJ/mol, respectively. However, Rh-CeO 2 exhibits superior sensitivity and satisfactory recovery time towards CO compared with Ag-CeO 2 and Au-CeO 2. Rh-CeO 2 also shows excellent selectivity to CO, which is a promising candidate material for CO sensors. The research conducted in this work is intended to contribute to dissolved gas analysis (DGA). [ABSTRACT FROM AUTHOR]

[Display omitted] • Formation energy and Goldschmidt tolerance factor of cation doped CsPbI 3 are calculated. • PBE, PBE + SOC and HSE06 are used to calculate the band structure. • Light absorption spectra of the doped CsPbI 3 all shows red shift. Among all inorganic perovskite, CsPbI 3 has the closest ideal bang gap for solar cells. However, the instability of metal halides hinders its commercial application. The doping of A-site organic cations might improve the stability of perovskite and also make the bandgap adjustable, thus enhance its photoelectric properties. In this paper, the structure stability, electronic structure and optical properties of CsPbI 3 with five different organic cations (H 3 NNH 2 +, CH 3 NH 3 +, C 3 H 6 NH 2 +, CH 3 NH 2 CH 3 +, CH 3 CH 2 NH 3 +) partially alternative doping instead the A site cation have been studied by using the first principles within density functional theory. In order to get accurate description of the bandstructure information, different exchange–correlation functions, e. g. PBE, PBE + SOC and HSE were used to describe the calculated systems, our calculation results indicated that the PBE exchange–correlation function can describe the electronic properties of the systems very well in this paper. Formation energy calculation indicates that all the doping systems are thermally stable; and all the doped CsPbI 3 show good tolerance factor according Goldschmidt rule. Among the five different doping systems, the bandgap of doped H 3 NNH 2 + will decrease to 1.28 eV, while the bandstructure of other doped cations will widen the bandgap. The maximum bandgap increased to 1.65 eV with doping C 3 H 6 NH 2 +. However, all the band structures are mainly contributed by inorganic framework. The results suggest that the structure doped organic cations can be exited stably, and the band edge of the optical absorption spectrum will be redshifted. These properties can provide research ideas for the subsequent research of A-site doping organic cations. [ABSTRACT FROM AUTHOR]

[Display omitted] The efficient search for crystals with targeted properties is a significant challenge in materials discovery. The rapidly growing field of materials informatics has so far primarily focused on the application of AI/ML models to predict the properties of known crystals from their fundamental and derived properties as descriptors. In the last few years, deep learning-based approaches have spawned a slew of innovative data-driven materials research applications. Materials scientists have used these techniques for the reverse engineering of crystal structures for target applications. However, one of the challenges has been the representation of the crystal structures in the machine readable format. Proposed representations in the literature lack in generality and scalability. In this paper, we train a conditional variational autoencoder with a scalable and invertible representation along with the elemental properties of the constituents as descriptors to inverse-design new crystal structures with specified attributes. When targeting formation energy, we show that our model predicts structures that are not in the complete OQMD database. Finally, we use first-principles density functional theory calculations to validate our findings and show that the developed model is able to generate novel crystal structures for targeted property, i.e. formation energy in this case. [ABSTRACT FROM AUTHOR]

The most significant difference between LiMn 2 O 4 and Li 2 MnO 3 is that LiMn 2 O 4 is not fully occupied structure and has synergies with fast-ion channels compared with Li 2 MnO 3. So, Li has a better migration ability in LiMn 2 O 4 than Li 2 MnO 3. [Display omitted] The basic physical properties of the layered lithium manganese oxide Li 2 MnO 3 and the spinel lithium manganese oxide LiMn 2 O 4 were investigated by the first-principles calculations based on density functional theory in this paper. Herein, the differences in crystal structures, electronic structures, and Li-ion transport characteristics between Li 2 MnO 3 and LiMn 2 O 4 were discussed in detail. It was found that both Li 2 MnO 3 and LiMn 2 O 4 are semiconductors, but the band gap of Li 2 MnO 3 (2.1 eV) is much larger than that of LiMn 2 O 4 (0.36 eV). The phonon dispersions show both the anti-ferromagnetic (AFM) configurations of Li 2 MnO 3 and LiMn 2 O 4 are dynamically stable. Moreover, the migration of Li ions in LiMn 2 O 4 is faster than that in Li 2 MnO 3. To further increase the Li diffusivity of Li 2 MnO 3 , the tetravalent dopants (Ir4+, Os4+, and Ru4+) were considered to substitute the Mn site of Li 2 MnO 3. Our theoretical results explain the higher stability of doped Li 2 MnO 3 through enhancing the Li migrating ability. [ABSTRACT FROM AUTHOR]

[Display omitted] Boron is added to stainless steels and polycrystalline superalloys to promote ductility, corrosion resistance, and retard creep at high temperatures (above 700 °C). However, the role of local atomic environment(s) and how boron may impact the interfacial properties between the Cr 23 C 6 precipitate, and the matrix are not fully understood. In the present work, we consider the face-centered cubic (FCC) Fe (0 0 1)/Cr 23 C 6 (0 0 1) interface as a model system to study how the interfacial local atomic structure and energy are affected by boron doping using first-principles density functional theory (DFT) calculations. The atomic structure(s) and energetics of a coherent interface were calculated by including the elastic contribution using linear elasticity theory combined with DFT. Boron has a significant effect on the interfacial energy and the atomic arrangement near the interface region through the increased covalent bonding, thereby ordering the interface and making any diffusion processes less favorable energetically. In turn, this may contribute to the retardation of the Cr 23 C 6 precipitate growth and coarsening. Because of the concurrent precipitation of the NbC, Cr 23 C 6 , and Sigma phases, it affects the whole cascade of microstructural evolution processes contributing to creep. More specifically, the ancillary additions of boron to the A-terminated Cr 23 C 6 significantly reduce the interfacial energy (∼0.09 J/m2) between the iron matrix and the Cr 23 C 6 : from ∼ 0.38 J/m2 without doping to 0.29 J/m2 with doping. This interfacial energy decrease due to boron additions is an important factor in interface strengthening and retarding creep in austenitic stainless steels. Such B-induced short-range order interface strengthening is facilitated via promoting interface covalence bonding and considerable local lattice distortion due to the atomic size misfit. These results were obtained using the new interface model described in the paper. This new interface model allows to quantitatively distinguish the highly complex asymmetric interface structures and corresponding interfacial energies and surface energies. [ABSTRACT FROM AUTHOR]

The theoretical applications of hyperbolic metamaterials have generated excitement in multiple fields, particularly in super-resolution optics. In practice, however, the potential of HMMs has been limited by the shortcomings of their constituent parts. Primarily, these are high losses and instabilities in the metal component, which is typically gold or silver, and excessive thickness in both types of layers. In this paper, we use density functional theory to investigate BaMoO 3 -SrHfO 3 metamaterials. We predict that these are hyperbolic throughout the visible range and have the potential to mitigate many of these limitations, including a significant loss reduction. We also examine the accuracy of the effective-medium theory, which is used to predict the bulk optical properties of multilayered HMMs. [Display omitted] • New class of hyperbolic metamaterials based on a layered perovskite/perovskite structure. • Lower losses compared to current metal plasmonic materials like gold and silver. • Comparison of the results of first-principles calculations with those of the effective-medium theory for layered metamaterials. [ABSTRACT FROM AUTHOR]

[Display omitted] Hydroxyapatite (HA) is the main component of human bones and teeth. HA has also been widely applied in various technological fields due to its unique properties. Reliable computational simulations for this critical material often require input parameters obtained from the first-principles density functional theory (DFT) calculations with appropriate exchange–correlation functionals. Previous DFT calculations are insufficient to verify the reliabilities of various functionals, particularly as they fail to assess predictions for multiple properties of the material. In this paper, we first select 18 different functionals to calculate geometric, elastic, electronic, and thermodynamic properties of hexagonal HA bulk crystal. We find that the results from optB86b-vdW and optB88-vdW functionals with dispersion corrections have the overall best agreement with available experimental data. Then, we choose optB88-vdW functional, as well as PBE functional without dispersion corrections as a comparison, to perform extensive first-principles DFT phonon calculations under the quasiharmonic approximation. Various thermodynamic properties (including phonon contributions to internal energy, entropy, and Helmholtz free energy) and thermal parameters (including thermal expansions of volume, thermal expansion coefficients, heat capacities, isothermal bulk moduli, etc.) versus temperature are consequently obtained. By comparing these quantities, we find that the results from optB88-vdW functional have significantly better agreement with available experimental data than those from PBE functional although the latter has been widely used in previous DFT calculations for HA-based material systems. [ABSTRACT FROM AUTHOR]

[Display omitted] • New ML models can significantly enhance the speed of identifying novel materials while incurring little computational expenses. • KRR and DeePMD models demonstrate proficiency in accurately estimating the formation energy within milliseconds. • A feature data set of binary magnesium alloys for ML training is provided. Density functional theory (DFT) have been widely used to screen thermodynamically stable material; however, its high computational cost limits its use. In this paper, we explore the use of DFT data from high-throughput calculations to create faster machine learning (ML) models that can be used to screen thermodynamically stable magnesium alloy materials. Our methods work by utilizing the kernel ridge regression (KRR) algorithm, as well as Deep Potential Molecular Dynamics (DeePMD) to train ML models for predicting the formation energy of magnesium alloys. The accuracy, stability, and generalization ability of the ML models created under both methods are evaluated in detail. Meanwhile, we have conducted in-depth comparative analysis of the two methods, which concluded that the accuracy of DeePMD model performs better and time efficiency of KRR model has more advantages. The results show that the best performing DeePMD model and KRR model achieve the RMSE of 0.43 meV/atom and 6.80 meV/atom, indicating that our methods provide a reliable idea for obtaining the formation energy of magnesium alloys. [ABSTRACT FROM AUTHOR]

The present paper reports the results of a set of first-principles calculations via Density Functional Theory to characterize the structural, electronic, and magnetic properties of the bulk and (001) surface structures of D0 22 -Mn 3 Ga. It was found that a Mn y -Ga-terminated and another Ga-terminated surfaces are most stable surface reconstructions. The role of C diffusion was evaluated. It has a very localized effect on the magnetic enhancement of these surfaces as it diffuses through them. The magnetic enhancement is also discussed in terms of the surface effect, the magnetoelastic effect, the superexchange interaction, and the Density of States of each atom. • MnGa- and Ga-terminated Mn 3 Ga(001) surfaces are stable at different growth conditions. • Surfaces possess a ferrimagnetic behavior dominated by the Mn atoms. • C atoms selectively incorporate in the Mn-only layers. • C atoms incorporate into the bulk and as subsurface species. • C incorporation generates a very localized effect on the magnetic moments. [ABSTRACT FROM AUTHOR]

[Display omitted] Harsh radiation environments cause displacement damages in semiconductor components, resulting in performance degradation. Molecular simulations provide a unique approach to study the dynamic processes of radiation-induced defect production, clustering, and evolution to design and reinforce novel semiconductor components. In this paper, we developed a more efficient machine learning (ML) potential with DFT accuracy to investigate the radiation damage in silicon material. The accuracy of the potential was verified by comparing the static properties, defect formation energy, and threshold displacement energy with experiments and DFT data. By simulating the excitation of a single PKA we found that with the ML potential PKA has an impact over a larger spatial area compared to the empirical potentials. Finally, by simulating multiple PKA excitations in sequence, we found that the first several excited PKAs produce an amorphous region. For the later excited PKAs, the energies are dissipated when they cross through the amorphous region, resulting in a 36% decrease in newly generated defects. [ABSTRACT FROM AUTHOR]

[Display omitted] Cubic SrTiO 3 is widely used as a model perovskite oxide due to its exotic optical, electronic, magnetic, superconducting, and ferroelectric properties, which are dominantly influenced by its lattice parameter. This paper reports on the experimental observation of Ta5+ dopant-induced expansion of the perpendicular lattice parameter (a ⊥) of (0 0 1) heteroepitaxial SrTi x Ta 1-x O 3 (x = 0–0.1) films grown at substrate temperature (T g) of 750 °C under 5 × 10-5 mbar oxygen partial pressure (p O 2). The experimental results demonstrate the synthesis of very high-quality epitaxial thin films in which almost all the Ta5+ dopants are substituted. However, not all the carriers are activated due to compensating defects. All these films have smooth surfaces and are stoichiometric in compositions. The value of a ⊥ increases linearly with increasing Ta or free carrier concentrations. This expansion is modelled via density functional theory (DFT). The influences of three effects are considered in the present modelling: (i) ionic radius of Ta dopant (ii) deformation potential due to hydrostatic strain energy (iii) concentration of free carrier. Further calculations of a ⊥ are also performed by considering the influence of substrate and the results overestimate the experimental values by ∼ 1 %. Further experiments on other perovskite systems are required to establish the efficacy of these modelling approaches. [ABSTRACT FROM AUTHOR]

To understand the magnetic properties of the novel two-dimensional materials and other phenomena which depend on electron correlation, finding the states located around Fermi energy (E F) and contributing to the correlated states of the system is basically important. In this paper, using the density functional theory, we investigate the electronic properties of substitution of 3 d transition metal (TM) atoms for Zr in trigonal prismatic 2 H and octahedral 1 T structure of monolayer TM-doped ZrSe 2 materials. The band structure, orbital projected density of states (DOS), and the number of electrons in the d -shell show that the correlated subspace depends strongly on the structural phase of the systems. The case of Mn substitution has the highest magnetic moment, 2.91 μ B and 3.30 μ B in 2 H and 1 T phases, respectively. In the 1 T structure of Mn-doped ZrSe 2 , almost partially filled t 2 g impurity bands is well separated from e g states in the non-magnetic calculation, having a small bandwidth of about W b = 0.4 eV. As a consequence, the strength of electron correlation U / W b increases (U is effective on-site Coulomb interaction) and puts this compound in the moderately or strongly correlated regime, and causes a stronger electron spin separation than the 2 H phase. In the 2 H phase, the correlated states include two impurity bands with dominantly d x y and d x 2 − y 2 characters and induce a smaller magnetic moment. Due to the large DOS of Mn and Cr atoms at the E F in both 1 T and 2 H structures, the Stoner criterion I. D (E F) > 1 is satisfied, which is reasonably consistent with our results of ferromagnetic total energy calculations and the sizable magnetic moments. [Display omitted] [ABSTRACT FROM AUTHOR]

[Display omitted] • The first-principles modelling based on the density functional theory was used to study the site dependent mechanical properties, such as elastic constants and related modulus for 3d transition metal doped MnV intermetallic. • The calculated properties show a dopant-site dependency. • Ni, Cu and Zn doping into MnV is a promising way to further improve ductility while maintaining appreciable hardness. • Data mining revealed that ductile materials show higher formation energies, suggesting that these systems are less stable. In this paper, the first-principles modeling based on the density functional theory (DFT) was used to explore the structural, mechanical, and thermodynamical properties of 3d transition metal-doped MnV intermetallic. The site-dependent mechanical properties such as elastic constants C ij , bulk modulus B , shear modulus G , Young's modulus E , Vickers hardness H V , Poisson's ratio ν , Cauchy pressure C 12 - C 44 , Pugh ratio B / G , and Debye temperature Θ D were systematically presented. Our study suggests that doping 3d metal elements into MnV is a promising way to tune the mechanical properties of the MnV intermetallic and further improve its intrinsic ductility while maintaining appreciable hardness. Data mining is also employed alongside DFT calculations to classify the hard/ductile systems and to study the interrelationships among the systems studied here and their physical properties in order to generate unexpected knowledge. [ABSTRACT FROM AUTHOR]

This paper is based on the study of structural, electronic, magnetic, elastic, phonon dynamics and thermodynamic properties of d 0 half-Heusler alloys GeKZ (Z = Ca, Sr) using the Density Functional Theory (DFT) approach. The investigation is carried out using the spin-polarized calculations with pseudopotential approach using Generalized Gradient Approximation by Perdew–Burke–Ernzerhof (GGA-PBE). Both the compounds GeKCa and GeKSr are found to be structurally stable in the ferromagnetic α -phase. The electronic properties in the form of spin-polarized band-structures and density of states of the compounds GeKCa and GeKSr show that they are half-metallic in nature with a finite and large half-metallic band-gap of 0.37 eV and 0.29 eV respectively in the spin-up states and a metallic behavior in the spin-down states with 100 % spin-polarization at the Fermi-level. Integral magnetic moment of 1.00 μ B is obtained for both the compounds which is in accordance with the Slater–Pauling Rule for half-Heusler alloys. The elastic properties show that the compounds are mechanically stable following Born stability criteria for cubic compounds and are found to be ductile in nature. The compounds have also been found to be dynamically stable against lattice vibrations on the basis of phonon dispersion calculations. They have also been explored regarding their thermodynamic properties using the quasi-harmonic approximation. They are also examined for the preservation of the integral magnetic moment against the lattice compression and expansion. The observed properties points towards the suitability of these compounds in the spintronics applications. [Display omitted] [ABSTRACT FROM AUTHOR]

[Display omitted] Three new Group IV element allotropes Fmmm C 72 , Si 72 , and Ge 72 are proposed in this paper, and their physical properties are researched by density functional theory (DFT). Each novel allotrope has mechanical, dynamical, and thermodynamic stability. The study of the elastic modulus shows that Fmmm Si 72 has greater elastic anisotropy than Fmmm C 72 and Ge 72. Fmmm C 72 is a superhard material with the hardness of 47.8 GPa. The investigation of the electronic band structures of Fmmm Si 72 and Ge 72 exhibits they have semiconducting properties, while Fmmm C 72 shows metallicity. Fmmm Si 72 has a direct band gap (1.41 eV), and it is very suitable for solar cells. In addition, Fmmm Si 72 has good absorption capacity in the visible light region, so Si 72 has possible applications in photovoltaic applications due to its direct band gap property and good absorption capacity in the visible light region. [ABSTRACT FROM AUTHOR]

Inevitably, all crystalline materials will contain imperfections that have the ability to modify the properties of the host material. Key to the development of advanced materials is the ability to predict the concentrations of different defects in any given environmental conditions and how the change in the defect population alters the material's properties. Modern first principles atomistic simulation techniques, such as density functional theory (DFT), are now widely employed for the simulation of point defects, however, to develop true insight into a material's defect chemistry, it is essential to link the energies calculated to thermodynamic variables that fully describe its operating conditions. The Defect Analysis Package (DefAP), an open-source Python code, has been designed to fulfil this role. The primary function of the package is to predict the concentrations of defects in materials as a function of key thermodynamic variables, such as temperature and availability of different species, expressed through chemical potentials. Through simple thermodynamic equations, DefAP allows the rapid exploration of a material's defect chemistry allowing direct comparison with experimental observations. Rapid exploration is supported through the use of autoplotting with carefully considered automatic data labelling and simplification options enabling production of publication quality plots. DefAP offers a wide range of options for the calculation of defect and carrier concentrations that can be customised by the user to suit the material studied and an extensive suite of options have been designed for the study of extrinsic defects (e.g. dopants or impurities). The capabilities of DefAP are demonstrated in this paper by studying intrinsic defects in YBa 2 Cu 3 O 7 , P doping in Si, Am accumulation in PuO 2 , and simultaneous build-up of T and He in Li 2 TiO 3. [Display omitted] • DefAP is a Python program facilitating exploration of a material's defect chemistry. • Predicts concentrations of point defects as function of key thermodynamic variables. • Features a customisable methodology and production of publication quality plots. • Extensive options available to study extrinsic defects (e.g. dopants or impurities). • Studying Si, PuO 2 , YBa 2 Cu 3 O 7 , and Li 2 TiO 3 demonstrate the capabilities of DefAP. [ABSTRACT FROM AUTHOR]

In this paper we have investigated the physical and transport properties of Bismuth Selenide, (Bi 2 Se 3) intercalated with Silver (Ag) (Gold (Au) is included for comparison) via dynamical mean field theory with local density approximation. The band structure of Bi 2 Se 3 with a Dirac cone is strongly influenced by the intercalation phenomena at moderate densities which eventually leads to an orbital selective metal insulator transition leading to superconductivity at low temperatures. The claims on the orbital selectivity are substantiated by computing the density of states. While the onset of superconducting correlations at low temperatures are supported by a sudden drop in the resistivity as a function of temperature and the nature of susceptibility. Further the Fermi surface (FS) maps for low and high temperature yields no FS reconstruction. [ABSTRACT FROM AUTHOR]

[Display omitted] • Electronic and electrochemical properties of Mn-doped Na 2 FePO 4 F are investigated by DFT calculations. • A significant decrease in the Na+ diffusion energy is observed for Na 2 Fe 0.5 Mn 0.5 PO 4 F. • A marked enhancement of average voltage and quality factor is seen for Na 2 Fe 0.5 Mn 0.5 PO 4 F. It is well known that fluorophosphate Na 2 FePO 4 F materials unveil promise in battery applications as sodium-ion cathode materials, particularly on account of their non-toxic features, economic and environmental advantages. In this paper, we mainly report on how the electronic conductivity and the voltage of the Na 2 FePO 4 F can be enhanced by Mn-doping, leading then to high energy density cathode materials. For this purpose, density functional theory (DFT) calculations are used to investigate the structural, electronic, and electrochemical properties of the pristine and the Mn-doped Na 2 FePO 4 F. The obtained results show a decrease of the band gap energy from 2.19 eV to 1.36 eV with the GGA + U approximation and from 2.80 eV to 1.93 eV with the HSE hybrid functional, by doping 50% of the Fe site in the Na 2 FePO 4 F lattice with Mn. This indicates an improvement in the electronic conductivity of the pristine material as confirmed by the calculation of effective masses. On the other hand, the Climbing Image Nudged Elastic Band (CI-NEB) calculations unveil a clear drop in the diffusion energies of the three similar paths in doped Na 2 FePO 4 F, which indicates the easy transportation of Na+ inside the lattice. The average voltage and quality factor-Q calculations reveal encouraging results for the 50% Mn-doped Na 2 FePO 4 F. These findings show the potential of Na 2 Fe 0.5 Mn 0.5 PO 4 F as a promising cathode material for Na-ion batteries. [ABSTRACT FROM AUTHOR]

In this paper we are going to present a computational study on the growth behaviour of Guinier–Preston zones (GPZ) in binary Al–Cu alloys. For this purpose we performed atomistic Monte-Carlo (MC) simulations and calculations of static configurational energies by applying a Cluster Expansion Hamiltonian for the binary Al–Cu system derived from density functional theory, which we already developed in a former publication. The MC simulations were designed to represent Cu diffusion and growth events of GPZ and were utilised to display atomistically resolved concentration fields and diffusion currents of mobile Cu in the vicinity of GPZ. For the static calculations we constructed sequences of configurations mimicking growth paths of Cu atoms and recorded their formation energy profiles. From both parts an energetically as well as kinetically stabilised mechanism for GPZ growth is proposed and the origin for the transition from GP(I) to GP(II) is elucidated. [Display omitted] • The growth of GPZ in Al–Cu alloys was studied atomistically and by MC simulations. • An energy barrier for arriving Cu atoms above and below the GPZ was found. • Due to the energy barrier growth is concentrated to GPZ edges. • GPZ growth was found to be kinetically and energetically stabilised. [ABSTRACT FROM AUTHOR]