查看更多>>摘要：The extension of interatomic potentials from elemental solids to compound ones causes a bottleneck in atomistic simulations of multi-component solids such as intermetallic compounds and solid solutions。 In contrast to several extensive tools released to construct elemental potentials， such as MEAMfit and ATOMICREX， very little software has been specifically designed for multi-component solids。 Herein， we extend our recently proposed software EAPOTs (Empirical interAtomic POTentials for single elemental solids) to interatomic potentials of compound solids。 This new software-termed as EAPOTc or the integrated Empirical interAtomic POTential optimization platform for compound solids-provides robust multi-level objective optimization strategies with various cross potential functions and extensive combinations of multiple targets such as energy， stress， force， and elasticity。 Compatibility with published elemental potentials was also implemented in EAPOTc to ensure seamless combinations of different sources of elemental potentials by using the transformation invariance rules without reliability loss for the original elemental potentials。 Similar to our EAPOTs code， a high-throughput (HT) scheme was designed based on automatic communication using first-principles code (e。g。， VASP) to retrieve the derived properties based on energy， stress， force， and elasticity; in addition， multiple objective optimization procedures were included。 The efficiency and flexibility of EAPOTc were critically validated and tested for various metallic and covalent compound systems， including HT implementation and applicability testing for extreme scenarios。 Our software demonstrated several advantages， such as a concise and user-friendly graphical user interface， extensive compatibility between elemental potentials， robust optimization schemes， and a high degree of functional integration。
查看更多>>摘要：Most of the research on perovskite materials rely on costly experiments or complex density functional theory (DFT) calculations to a large extent。 In contrast， machine learning (ML) combined with data mining is more effective in predicting perovskite properties。 In this work， by mining data from the Materials Project database and other materials databases， we constructed a raw data set containing the ABO3-type compounds calculated by density functional theory (DFT) and generated a feature set based on multi-scale descriptors including compound properties and component element attributes。 By comparing various machine learning models， the optimized support machine regression (SVR) model， Particle swarm optimization-support machine regression (PSO-SVR) were used to predict the energy above the convex hull (Ehull) of ABO3-type compounds that is the criteria for thermodynamic stability of ABO3-type compounds。 In addition， the important descriptors that have significant influence on the thermodynamic stability of ABO3-type compounds were screened out， and the relationship between these descriptors and Ehull was discussed。 Finally， the stable and ideal ABO3 compounds were screened out for perovskite candidates。
查看更多>>摘要：In this work we study the effects of C and Cr enrichment of (100) dislocation loops (DL) on their absorption and obstacle strength when interacting with an edge dislocation。 To do so， we have i) developed a C-Cr cross potential based on density functional theory data as part of a ternary FeCrC interatomic potential; ii) performed exchange Monte Carlo simulations employing the developed interatomic potential to obtain the distribution of the solutes enriching the DL in the energetically optimum configurations; iii) performed large scale molecular dynamics simulations employing the interatomic potential to characterize the interaction between an edge dislocation line and the decorated DL。 We found that the obstacle stress scales to the same obstacle strength regardless the DL density。 On the other hand， we found that C， the level of Cr enrichment， loop size and interaction temperature have a significant impact on the obstacle strength and level of absorption of the loops。 The presented results can be used to help parameterize and validate discrete dislocation dynamics codes and therein integrated constitutive laws to enable accounting for irradiation-induced chemical segregation effects。
查看更多>>摘要：Shape memory alloys (SMAs) are desirable candidates for elastocaloric effect materials， but they all suffer from large thermal hysteresis (T-hys)。 This study analyzes multicomponent TiNi-based SMAs dataset by machine learning (ML) to explore new SMAs with narrow T-hys。 The second-largest eigenvalue lambda(2) of the stretch trans-formation matrix U is added to the original dataset to guide the ML process as a feature。 Firstly， lambda(2) is obtained by first-principles calculations combined with ML。 XGBoost Regressor (XGBR) combined with Leave-One-Out Cross -Validation (LOO-CV) is selected from four algorithms for modeling with the highest coefficient of determination R-2 of 0。87。 The introduction of lambda(2) improves the performance of the model。 The dataset is divided into 15 groups based on different doping elements (such as Hf， Cu， Zr， etc。)， among which TiNiCu is the most predictive component with the R-2 of 0。89。 Over 500 TiNiCu components are randomly generated and predicted T-hys。 Based on the contour maps created from the prediction results， it is found that T-hys is likely to decrease with the in-crease of Cu doping in general， and minimum T-hys occurs when the Cu is about 15 at。 %， which is consistent with the existing experimental results。 Eventually， a potential Thys minimum (1。2 K) region of TixNiyCuz (58。3%<= x <= 58。5%， 26。5% <= y <= 27%， 14。8% <= z <= 15。3%， x +y +z =100%) SMA composition is predicted。 Our study not only provides a potential selection of narrow T-hys TiNi-based SMAs but also indicates combining of XGBoost and DFT calculation is an effective strategy for materials design。
查看更多>>摘要：Thin-film coatings can be found everywhere in modern technological applications due to desirable electrical， mechanical， chemical， and optical properties。 These properties directly depend upon the thin-film's microstruc-tural features， which are themselves influenced by the materials and vapor-deposition processing conditions used for fabrication。 As such， understanding processing-microstructure relationships is essential to designing thin-films with optimized properties， and discovering new processing conditions that allow for novel thin-films with multifunctional microstructures。 Here， a short review is presented on recent developments that utilize the phase-field method to simultaneously model the vapor-deposition process and corresponding microstructure formation at the mesoscale。 Phase-field-based vapor-deposition models that simulate thin-film growth of immiscible alloy and polycrystalline systems are highlighted in addition to machine-learning-based surrogate models that can facilitate accelerated high-fidelity simulations along with materials design and exploration studies。
查看更多>>摘要：Expensive-to-train deep learning models can benefit from an optimization of the hyperparameters that determine the model architecture。 We optimize 23 hyperparameters of a materials informatics model， Compositionally-Restricted Attention-Based Network (CrabNet)， over 100 adaptive design iterations using two models within the Adaptive Experimentation (Ax) Platform。 This includes a recently developed Bayesian optimization (BO) algorithm， sparse axis-aligned subspaces Bayesian optimization (SAASBO)， which has shown exciting performance on high-dimensional optimization tasks。 Using SAASBO to optimize CrabNet hyperparameters， we demonstrate a new state-of-the-art on the experimental band gap regression task within the materials informatics benchmarking platform， Matbench (similar to 4。5 % decrease in mean absolute error (MAE) relative to incumbent)。 Characteristics of the adaptive design scheme as well as feature importances are described for each of the Ax models。 SAASBO has great potential to both improve existing surrogate models， as shown in this work， and in future work， to efficiently discover new， high-performing materials in high-dimensional materials science search spaces。
查看更多>>摘要：Al2O3 based solar selective absorbing coatings have been considered as a promising candidate for solar thermal conversions， but their properties are expected to be improved urgently。 Herein， inspired by the composite materials design， an optimization method for optical performance and thermal stability of Al2O3 was explored by us， the W-Al2O3 based composite structures used for solar was developed， and its electronic structure and optical performance were investigated by the first-principles calculations。 It was found that the band gap of the proposed composite structure can be successfully regulated and it decreases significantly with the increase of the mass fraction of W doped in Al2O3， which leads to the improvement of the thermal stability。 The optical properties and solar thermal conversion properties of W-Al2O3 based selective absorption coating are accurately predicted。 The results show that W-Al2O3 based solar selective absorption coating also has good absorption and reflection properties。 The addition of W atom leads to the reduction of solar thermal efficiency。 When the W mass fraction doped in W-Al2O3 is 23。92% at the temperature of 273 K， compared with pure Al2O3， the solar thermal conversion efficiency is reduced by 6。56%， and the corresponding absorption rate and thermal radiation was reduced by 6。54% and 1。99%， respectively。
查看更多>>摘要：The optimization of properties of perovskite oxides has drawn interest on account of their diverse areas of application。 In this work， the hierarchical clustering technique is used to reduce the multi-collinearity among selected features from literature that are reported to have an effect on perovskite formation and stability。 Operating on the vast composition space of double oxide perovskite compositions available in literature and online repositories， in this manuscript， an attempt has been made to extract the relationship between the composition and structure to predict their formability and stability。 Machine learning (ML) classifiers are trained on these datasets to predict novel stable perovskite compositions。 The study uses a vast feature space to narrow down the most important factors affecting the formability and stability in perovskite compounds。 It also identifies stable compositions that have band gaps suitable for photovoltaic and photocatalytic applications。 The developed random forest (RF)-based models may be extended to include the implications beyond photosensitive applications by focusing on the physico-chemical mechanisms driving the phenomena behind each application。
查看更多>>摘要：Spin-lattice dynamics is used to study the magnetic properties of Fe foams。 The temperature dependence of the magnetization in foams is determined as a function of the fraction of surface atoms in foams， nsurf。 The Curie temperature of foams decreases approximately linearly with nsurf， while the critical exponent of the magnetization increases considerably more strongly。 If the data are plotted as a function of the fraction of surface atoms， reasonable agreement with recent data on vacancy-filled Fe crystals and novel data on void filled crystals is observed for the critical temperature。 Critical temperature and critical exponent also depend on the coordination of surface atoms。 Although the decrease we find is relatively small， it hints to the possibility of improved usage of topology to taylor magnetic properties。
查看更多>>摘要：Considering the mismatch of lattice constants between the substrate and the (S， Se， Te) double-doped ZnO system and of the thermal expansion coefficients between the substrate and the doped system， the doped system was subject to external strain。 Previous studies have neglected this issue。 In the current research， we first applied biaxial strain on the model and then performed density functional theory calculation。 The effects of applying different strains on the stability of the doped system， the red shift of the absorption spectrum， the trap effect， and the carrier lifetime were determined。 The research results showed that Zn36SSeO34， Zn36STeO34， and Zn36SeTeO34 systems had smaller formation energies and better stability under the Zn-rich condition than under the Orich condition。 All doped systems had negative cohesive energies and good stability。 With the synergistic effect of strain and doping， when the biaxial compressive strain of the Zn36STeO34 system was - 5%， the hole-electron separation was good， the trap effect was substantial， the hole life increased， the absorption spectrum had a better red shift， and the oxidation reaction improved。 These findings had a certain theoretical guiding effect on photocatalysts for the decomposition of water for oxygen production。
NSTL
Elsevier