SiCp/Al composites offer the advantages of lightweight construction, high strength, and corrosion resistance, rendering them extensively applicable across various domains such as aerospace and precision instrumentation. Nonetheless, the interfacial reaction between SiC and Al under high temperatures leads to degradation in material properties. In this study, the interface segregation energy and interface binding energy subsequent to the inclusion of alloying elements were computed through a first-principle methodology, serving as a dataset for machine learning. Feature descriptors for machine learning undergo refinement via feature engineering. Leveraging the theory of machine-learning-accelerated first-principle computation, six machine learning models-RBF, SVM, BPNN, ENS, ANN, and RF-were developed to train the dataset, with the ANN model selected based on R2 and MSE metrics. Through this model, the accelerated computation of interface segregation energy and interface binding energy was achieved for 89 elements. The results indicate that elements including B, Si, Fe, Co, Ni, Cu, Zn, Ga, and Ge exhibit dual functionality, inhibiting interfacial reactions while bolstering interfacial binding. Furthermore, the atomic-scale mechanism elucidates the interfacial modulation of these elements. This investigation furnishes a theoretical framework for the compositional design of SiCp/Al composites.
In this research, the adsorption performance of individual atoms on the surface of monolayer graphene surface was systematically investigated using machine learning methods to accelerate density functional theory. The adsorption behaviors of over thirty different atoms on the graphene surface were computationally analyzed. The adsorption energy and distance were extracted as the research targets, and the basic information of atoms (such as atomic radius, ionic radius, etc.) were used as the feature values to establish the dataset. Through feature engineering selection, the corresponding input feature values for the input-output relationship were determined. By comparing different models on the dataset using five-fold cross-validation, the mathematical model that best fits the dataset was identified. The optimal model was further fine-tuned by adjusting of the best mathematical ML model. Subsequently, we verified the accuracy of the established machine learning model. Finally, the precision of the machine learning model forecasts was verified by the method of comparing and contrasting machine learning results with density functional theory. The results suggest that elements such as Zr, Ti, Sc, and Si possess some potential in controlling the interfacial reaction of graphene/aluminum composites. By using machine learning to accelerate first-principles calculations, we have further expanded our choice of research methods and accelerated the pace of studying element-graphene interactions.
In order to provide guidance for furthering the balance of strength and toughness of AerMet 100 steel through tempering treatment, the effects of the tempering time on microstructure and mechanical properties are investigated. The microstructure evolution, especially M2C precipitates and austenite in AerMet 100 tempered at 482 °C for 1~20 h, was characterized, and its influences on the mechanical properties were studied. The tensile strength decreases gradually, the yield strength increases first and then decreases, and the fracture toughness KIC increases gradually with an increasing tempering time. The strength and toughness matching of AerMet 100 steel is achieved by tempering at 482 °C for 5~7 h. Without considering the martensitic size effect, the influence of the dislocation density on the tensile strength is more significant during tempering at 482 °C. The precipitation strengthening mechanism plays a dominant role in the yield strength when tempering for 5 h or less, and the combined influence of carbide coarsening and a sharp decrease in the dislocation density resulted in a significant decrease in tensile strength when tempering for 8 h or more. The fracture toughness KIC is primarily influenced by the reverted austenite, so that KIC increases gradually with the prolongation of the tempering time. However, a significant decrease in the dislocation density resulting from long-term tempering has a certain impact on KIC, giving rise to a decrease in the rising amplitude in KIC after tempering for 8 h or more.
In this paper, we studied the effects of a series of alloying atoms on the stability and micromechanical properties of aluminum alloy using a machine learning accelerated first-principles approach. In our preliminary work, high-throughput first-principles calculations were explored and the solution energy and theoretical stress of atomically doped aluminum substrates were extracted as basic data. By comparing five different algorithms, we found that the Catboost model had the lowest RMSE (0.24) and lowest MAPE (6.34), and this was used as the final prediction model to predict the solid solution strengthening of the aluminum matrix by the elements. Calculations show that alloying atoms such as K, Na, Y and Tl are difficult to dissolve in the aluminum matrix, whereas alloy atoms like Sc, Cu, B, Zr, Ni, Ti, Nb, V, Cr, Mn, Mo, and W exerted a strengthening influence. Theoretical studies on solid solutions and the strengthening effect of various alloy atoms in an aluminum matrix can offer theoretical guidance for the subsequent selection of suitable alloy elements. The theoretical investigation of alloy atoms in an aluminum matrix unveils the fundamental aspects of the solution strengthening effect, contributing significantly to the expedited development of new aluminum alloys.
The FeNiCrAlCoCuTi alloy system has great advantages in mechanical properties such as high hardness and toughness. It has high performance potential and research value and the key in research is designing alloy compositions with target properties. The traditional method, experimental analysis, is highly inefficient to properly exploit the intrinsic relationship between material characteristics and properties for multi-component alloys, especially in investigating the whole composition space. In this work, we present a research way that uses first principles calculation to obtain the properties of multi-component alloys and uses machine learning to accelerate the research. The FeNiCrAlCoCuTi alloy system with its elastic properties is used as an example to demonstrate this process. We specifically design models for each output, all of which have RMSE values of less than 1.1, and confirm their effectiveness through experimental data in the literature, showing that the relative error is below 5%. Additionally, we perform an interpretable analysis on the models, exposing the underlying relationship between input features and output. By means of spatial transformation, we achieve the prediction of the full-component spatial performance from binary to multiple components. Taking the FeNiCrAlM (M = Co, Cu, Ti) quinary alloy system as an example, we design a single-phase BCC structure composed of an Fe0.23Cr0.23Al0.23Ni0.03Cu0.28 alloy with a Young's modulus of 273.10 GPa, as well as a single-phase BCC structure composed of an Fe0.01Cr0.01Al0.01Ni0.44Co0.53 alloy with a shear modulus of 103.6 GPa. Through this research way, we use machine learning to accelerate the calculation, which greatly shortens research time and costs. This work overcomes the drawbacks of traditional experiments and directly obtains element compositions and composition intervals with excellent performance.
This paper mainly used database technology, machine learning, thermodynamic calculation, experimental verification, etc., on integrated computational materials engineering. The interaction between different alloying elements and the strengthening effect of precipitated phases were investigated mainly for martensitic ageing steels. Modelling and parameter optimization were performed by machine learning, and the highest prediction accuracy was 98.58%. We investigated the influence of composition fluctuation on performance and correlation tests to analyze the influence of elements from multiple perspectives. Furthermore, we screened out the three-component composition process parameters with composition and performance with high contrast. Thermodynamic calculations studied the effect of alloying element content on the nano-precipitation phase, Laves phase, and austenite in the material. The heat treatment process parameters of the new steel grade were also developed based on the phase diagram. A new type of martensitic ageing steel was prepared by selected vacuum arc melting. The sample with the highest overall mechanical properties had a yield strength of 1887 MPa, a tensile strength of 1907 MPa, and a hardness of 58 HRC. The sample with the highest plasticity had an elongation of 7.8%. The machine learning process for the accelerated design of new ultra-high tensile steels was found to be generalizable and reliable.
High-entropy alloys have gained widespread concern in response to the increased requirements for future high-temperature structural superalloys. By combining phase-diagram calculations with microhardness, compression behavior measurements at room temperature, and elevated temperature conditions, the very important role of the Cr element on the microstructure and properties is deeply revealed, which provides candidates materials for future high-temperature alloy applications. The increment of Cr favors the regulation of the two-phase fraction and distribution. The thermodynamic calculations illustrate that the density and melting point of the HEAs showed an increasing trend with the increase of the Cr content. The typical worm-like microstructure of the Cr0.6 alloy with a dual BCC structure was detected. Meanwhile, on the one hand, the increment of the Cr elements results in a considerable optimization of the mechanical properties of the alloy in terms of strength and ductility at room temperature. The corresponding compressive strength and plasticity of Cr0.6 alloy at room temperature are 3524 MPa and 43.3%. On the other hand, the high-temperature mechanical properties of the alloy are greatly enhanced. At 1000 °C, the yield strength of the Cr0.6 alloy is about 25 MPa higher than that of the Cr0.4 alloy. The superior mechanical properties are attributed to the pronounced work-hardening response, and the work-hardening behavior of Cr-containing HEAs was systematically analyzed by employing the modified Ludwik model. The higher content of Cr helps the resistance of the local deformation response, improving the nonuniform strain and promoting the balance of strength and ductility of the alloys.
Graphene has attracted significant interest due to its unique properties. Herein, we built an adsorption structure selection workflow based on a density functional theory (DFT) calculation and machine learning to provide a guide for the interfacial properties of graphene. There are two main parts in our workflow. One main part is a DFT calculation routine to generate a dataset automatically. This part includes adatom random selection, modeling adsorption structures automatically, and a calculation of adsorption properties. It provides the dataset for the second main part in our workflow, which is a machine learning model. The inputs are atomic characteristics selected by feature engineering, and the network features are optimized by a genetic algorithm. The mean percentage error of our model was below 35%. Our routine is a general DFT calculation accelerating routine, which could be applied to many other problems. An attempt on graphene/magnesium composites design was carried out. Our predicting results match well with the interfacial properties calculated by DFT. This indicated that our routine presents an option for quick-design graphene-reinforced metal matrix composites.
Graphene has become an ideal reinforcement for reinforced metal matrix composites due to its excellent mechanical properties. However, the theory of graphene reinforcement in graphene/aluminum matrix composites is not yet well developed. In this paper, the effect of different temperatures on the mechanical properties of the metal matrix is investigated using a classical molecular dynamics approach, and the effects of the configuration and distribution of graphene in the metal matrix on the mechanical properties of the composites are also described in detail. It is shown that in the case of a monolayer graphene-reinforced aluminum matrix, the simulated stretching process does not break the graphene as the strain increases, but rather, the graphene and the aluminum matrix have a shearing behavior, and thus, the graphene "pulls out" from the aluminum matrix. In the parallel stretching direction, the tensile stress tends to increase with the increase of the graphene area ratio. In the vertical stretching direction, the tensile stress tends to decrease as the percentage of graphene area increases. In the parallel stretching direction, the tensile stress of the system tends to decrease as the angle between graphene and the stretching direction increases. It is important to investigate the effect of a different graphene distribution in the aluminum matrix on the mechanical properties of the composites for the design of high-strength graphene/metal matrix composites.
Precipitation hardening stainless steels have attracted extensive interest due to their distinguished mechanical properties. However, it is necessary to further uncover the internal quantitative relationship from the traditional standpoint based on the statistical perspective. In this review, we summarize the latest research progress on the relationships among the composition, microstructure, and properties of precipitation hardened stainless steels. First, the influence of general chemical composition and its fluctuation on the microstructure and properties of PHSS are elaborated. Then, the microstructure and properties under a typical heat treatment regime are discussed, including the precipitation of B2-NiAl particles, Cu-rich clusters, Ni3Ti precipitates, and other co-existing precipitates in PHSS and the hierarchical microstructural features are presented. Next, the microstructure and properties after the selective laser melting fabricating process which act as an emerging technology compared to conventional manufacturing techniques are also enlightened. Thereafter, the development of multi-scale simulation and machine learning (ML) in material design is illustrated with typical examples and the great concerns in PHSS research are presented, with a focus on the precipitation techniques, effect of composition, and microstructure. Finally, promising directions for future precipitation hardening stainless steel development combined with multi-scale simulation and ML methods are prospected, offering extensive insight into the innovation of novel precipitation hardening stainless steels.
Near-infrared (NIR) synaptic devices integrate NIR optical sensitivity and synaptic plasticity, emulating the basic biomimetic function of the human visual system and showing great potential in NIR artificial vision systems. However, the lack of semiconductor materials with appropriate band gaps for NIR photodetection and effective strategies for fabricating devices with synaptic behaviors limit the further development of NIR synaptic devices. Here, a two-terminal NIR synaptic device consisting of the In2Se3/MoS2 heterojunction has been constructed, and it exhibits fundamental synaptic functions. The reduced band gap and potential barrier of In2Se3/MoS2 heterojunctions are essential for NIR synaptic plasticity. In addition, the NIR synaptic properties of In2Se3/MoS2 heterojunctions under strain have been studied systematically. The ΔEPSC of the In2Se3/MoS2 synaptic device can be improved from 38.4% under no strain to 49.0% under a 0.54% strain with a 1060 nm illumination for 1 s at 100 mV. Furthermore, the artificial NIR vision system consisting of a 10 × 10 In2Se3/MoS2 device array has been fabricated, exhibiting image sensing, learning, and storage functions under NIR illumination. This research provides new ideas for the design of flexible NIR synaptic devices based on 2D materials and presents many opportunities in artificial intelligence and NIR vision systems.
Artificial Intelligence , Molybdenum , Humans , Biomimetics , Learning , Synapses
A new carbon allotrope, pentadiamond, was recently reported in the literature. Herein, we investigate its thermal expansion and thermoelastic properties by first principles. It is observed that the bulk modulus and hardness of pentadiamond are far less than those of diamond, but the thermal expansion of pentadiamond is lower than that of diamond in the range of 0 K to 2000 K, and even negative in the temperature range of 0-190 K. The negative thermal expansion at low temperature originates from the transverse vibrations of the edge-shared atoms in the coplanar double-pentagon. The low thermal expansion at high temperature is contributed by the strong bonds in pentadiamond. Benefiting from the low thermal expansion, the elastic constants of pentadiamond decrease very slowly with respect to temperature compared with those of diamond. The low sensitivity of thermodynamic and thermoelastic properties to temperature makes pentadiamond a promising material for high anti-thermal-shock and accurate electronic device applications.
Dilute magnetic semiconductors (DMSs), such as (In, Mn)As and (Ga, Mn)As prototypes, are limited to III-V semiconductors with Curie temperatures (T c) far from room temperature, thereby hindering their wide application. Here, one kind of DMS based on perovskite niobates is reported. BaM x Nb(1-x)O3-δ (M = Fe, Co) powders are prepared by the composite-hydroxide-mediated method. The addition of M elements endows BaM x Nb(1-x)O3-δ with local ferromagnetism. The tetragonal BaCo x Nb(1-x)O3-δ nanocrystals can be obtained by Co doping, which shows strong saturation magnetization (M sat) of 2.22 emu g-1, a remnant magnetization (M r) of 0.084 emu g-1 and a small coercive field (H c) of 167.02 Oe at room temperature. The ab initio calculations indicate that Co doping could lead to a 64% local spin polarization at the Fermi level (E F) with net spin DOS of 0.89 electrons eV-1, this result shows the possibility of maintaining strong ferromagnetism at room temperature. In addition, the trade-off effect between the defect band absorption and ferromagnetic properties of BaM x Nb(1-x)O3-δ is verified experimentally and theoretically.
In the gel-casting process, the proper selection of technological parameters is crucial for the final quality of a green body. In this work, the finite element method is used to investigate the mold characteristics in the gel-casting process, and the typical flow behaviors under different conditions are presented. Based on the distribution characteristics of temperature, pressure and flow field of gel polymer, the simulated results provide some possible reasons for the generation mechanisms of defects. Then, a series of simulations were performed to investigate the effect of process parameters on the molding quality of green gel-cast bodies. The results show that the decreasing loading speed can effectively reduce the number of defects and improve the molding quality. In addition, this paper presents a new technique by applying the exhaust hole to decrease the number of defects and, hence, improve structural integrity. The influence of the loading speed on the mold characteristics is well understood for the gating system with an exhaust hole, which suggests to us appropriate parameters for optimizing the molding design. This work provides a theoretical basis to explicate the generating mechanism of defects involved in the gel-casting process and acquires an optimized technique to produce a silicon carbide green body.
Here, we report a novel topotactic method to grow 2D free-standing perovskite using KNbO3 (KN) as a model system. Perovskite KN with monoclinic phase, distorted by as large as â¼6 degrees compared with orthorhombic KN, is obtained from 2D KNbO2 after oxygen-assisted annealing at relatively low temperature (530 °C). Piezoresponse force microscopy (PFM) measurements confirm that the 2D KN sheets show strong spontaneous polarization (Ps) along [101Ì ]pc direction and a weak in-plane polarization, which is consistent with theoretical predictions. Thickness-dependent stripe domains, with increased surface displacement and PFM phase changes, are observed along the monoclinic tilt direction, indicating the preserved strain in KN induces the variation of nanoscale ferroelectric properties. 2D perovskite KN with low symmetry phase stable at room temperature will provide new opportunities in the exploration of nanoscale information storage devices and better understanding of ferroelectric/ferroelastic phenomena in 2D perovskite oxides.
Material genetic engineering studies the relationship between the composition, microstructure, and properties of materials. By adjusting the atomic composition, structure, or configuration of the material and combining different processes, new materials with target properties obtained. In this paper, the design, and properties of the ordered phases in Fe25Cr25Ni25TixAl(25-x) (subscript represents the atomic percentage) multi-principal element alloys are studied. By adjusting the percentages of Ti and Al atoms, the effect of the atomic percentage content on ordered phases' structural stability in multi-principal element alloys are studied. Thermodynamic analysis predicted the composition phase and percentage of the alloy. Formation heat, binding energy, and elastic constants confirmed the structural stability and provide a theoretical basis for designing alloys with target properties. The results showed that the disordered BCC A2 phase and the ordered BCC B2 phase are the ductile phases, while the Laves phase is brittle. The research method in this paper is used to design multi-principal element alloys or other various complex materials that meet the target performance.
The fundamental challenge for creating the crystal structure model used in a multi-principle element design is the ideal combination of atom components, structural stability, and deformation behavior. However, most of the multi-principle element alloys contain expensive metallic and rare earth elements, which could limit their applicability. Here, a novel design of low-cost AlCrTiFeNi multi-principle element alloy is presented to study the relationship of structure, deformation behavior, and micro-mechanism. This structured prediction of single-phase AlCrTiFeNi by the atomic-size difference, mixing enthalpy ΔHmix and valence electron concentration (VEC), indicate that we can choose the bcc-structured solid solution to design the AlCrTiFeNi multi-principle element alloy. Structural stability prediction by density functional theory calculations (DFT) of single phases has verified that the most advantageous atom occupancy position is (FeCrNi)(AlFeTi). The experimental results showed that the structure of AlCrTiFeNi multi-principle element alloy is bcc1 + bcc2 + L12 phases, which we propose as the fundamental reason for the high strength. Our findings provide a new route by which to design and obtain multi-principle element alloys with targeted properties based on the theoretical predictions, first-principles calculations, and experimental verification.
Preparing transition-metal oxides in their two-dimensional (2D) form is the key to exploring their unrevealed low-dimensional properties, such as the p-type transparent superconductivity, topological Mott insulator state, existence of the condensed 2D electron/hole gas, and strain-tunable catalysis. However, existing approaches suffer from the specific constraint techniques and precursors that limit their product types. Here, we report a solution-based method to directly synthesize KNbO2 in 2D by an out-of-the-pot growth process at low temperature, which is observed directly in real time. The developed method can also be applied to other 2D ternary oxide syntheses, including CsNbO2 and composited NaxK1-xNbO2, and it can be extended to the preparation of self-assembled nanofilms. In addition, We demonstrate the emission of broadband photoluminescence (PL, λ â¼ 350-800 nm) from as-synthesized single-crystal 2D KNbO2 sheets down to a single unit cell thickness. The ultra-broadband emission is ascribed to the self-trapped excitation state (STEs) from the in-phase distortion of the NbO6 octahedrons in 2D NbO2- layers. Beyond the broader luminescent range and the robust material thermal stability of niobates, the absence of sample size restrictions and the large aspect ratio of the 2D oxide sheets will provide opportunities in miniaturizing and advancing 2D-materials integrated optoelectronic devices.
We study the spin-orbit interaction of two-dimensional electron/hole gas (2DEGs/2DHGs) on quasi-2D potassium niobates (KNs) via first-principles calculations. The strong surface polarity changes the free surface states from 2DEGs to 2DHGs. The in-plane dipole maintained on 2D models leads to giant Zeeman-type spin splitting, as high as 566 meV for the (001)c facet KN and 1.21 eV for the (111)c facet KN. The thickness-dependent Zeeman-type spin splitting shows a linear relation with respect to 1/r, while the corresponding in-plane polarization quantum has a linear relation of 1/(2^0.5)with respect to a decrease in thickness. Interestingly, the 2DHGs with molecular-like orbital character is solely constituted by O 2p states, showing logic switchable behavior at extremely thin samples with enormous Zeeman-type splitting that can switch between insulator and conductor by opposite spin polarization.
The Polar discontinuity at heterointerface and the bare surface reconstructs the electronic phase of perovskite oxides. This gives rise to confined free electrons which intrinsically transit material from band insulator to metal. However, the insulator-metal transition induced by free holes has not been investigated so far due to the challenge in obtaining free hole state in oxides. Here, we propose a simple method whereby free holes can be obtained via polar facet reorientation. In the high polarity case, free holes can be supported by the lift up of O 2p subbands, which split into three independent subbands (one heavy hole subband and two light hole subbands) due to strong quantum confinement. Results show that both of the free electron and hole states are confined in a two dimensional quantum well, subjecting to the confined energy (E - E F) and occupied density of states around the Fermi level, indicating a finite thickness for preserving the metal states.
