Design of Multicomponent Alloys with Single C14 Laves Phase for Hydrogen Storage assisted by Computational Thermodynamic

Design methods with predictive properties modelling are paramount tools to explore the vast compositional field of multicomponent alloys. The applicability of an alloy as a hydrogen storage media is governed by its pressure-composition-temperature (PCT) diagram. Therefore, the prediction of PCT diagrams for multicomponent alloys is fundamental to design alloys with optimized properties for hydrogen storage applications. In this work, a strategy to design single C14-type Laves phase multicomponent alloys for hydrogen storage assisted by computational thermodynamic is presented. Since electronic and geometrical factors play an important role in the formation and stability of multicomponent Laves phase, valence electron concentration (VEC), atomic radius ratio (r_A/r_B), and atomic size mismatch (δ) are initially considered to screen a high number of compositions and find alloy systems prone to form Laves phase structure. Then, CALPHAD method was employed to find 142 alloys of the (Ti, Zr or Nb)(Cr, Mn, Fe, Co, Ni, Cu, or Zn)2 system predicted to crystallize as single C14 Laves phase structure. In addition, we present a thermodynamic model to calculate PCT diagrams of C14 Laves phase alloys based solely on the alloy’s composition. In this work, the entropy and enthalpy of hydrogen solution in the C14 Laves phase were modelled considering that hydrogen solid solution occurs only at the A2B2-type interstitial sites of the C14 Laves phase structure. Experimental pressure-composition-isotherm (PCI) diagrams of six C14 Laves phase alloys were compared against the calculated ones resulting in a good prediction capability. Therefore, the room temperature PCI diagrams of 142 single C14 Laves phase multicomponent alloys were calculated. The results show that single C14 Laves phase multicomponent alloys within a wide range of equilibrium pressure at room temperature can be obtained, being promising candidates for different hydrogen storage applications, such as room temperature tanks, hybrid tanks and Ni-metal hydrides batteries.

pyMPEALab Toolkit for Accelerating Phase Design in Multi-principal Element Alloys

Multi-principal element alloys (MPEAs) occur at or nearby the centre of the multicomponent phase space, and they have the unique potential to be tailored with a blend of several desirable properties for the development of materials of future. The lack of universal phase diagrams for MPEAs has been a major challenge in the accelerated design of products with these materials. This study aims to solve this issue by employing data-driven approaches in phase prediction. A MPEA is first represented by numerical fingerprints (composition, atomic size difference , electronegativity , enthalpy of mixing , entropy of mixing , dimensionless $$\Omega$$ Ω parameter, valence electron concentration and phase types ), and an artificial neural network (ANN) is developed upon the datasets of these numerical descriptors. A pyMPEALab GUI interface is developed on the top of this ANN model with a computational capability to associate composition features with remaining other input features. With the GUI interface, an user can predict the phase(s) of a MPEA by entering solely the information of composition. It is further explored on how the knowledge of phase(s) prediction in composition-varied $$\hbox {Al}_x$$ Al x CrCoFeMnNi and $$\hbox {CoCrNiNb}_x$$ CoCrNiNb x can help in understanding the mechanical behavior of these MPEAs. Graphic Abstract

Revealing and Controlling the Core of Screw Dislocations in BCC Metals

Body-centred-cubic (BCC) transition metals (TMs) tend to be brittle at low temperatures, posing significant challenges in their processing and major concerns for damage tolerance in critical load-carrying applications. The brittleness is largely dictated by the screw dislocation core structure; the nature and control of which has remained a puzzle for nearly a century. Here, we introduce a universal model and a physics-based material index χ that guides the manipulation of dislocation core structure in all pure BCC metals and alloys. We show that the core structure, commonly classified as degenerate (D) or non-degenerate (ND), is governed by the energy difference between BCC and face-centred cubic (FCC) structures and χ robustly captures this key quantity. For BCC TMs alloys, the core structure transition from ND to D occurs when χ drops below a threshold, as seen in atomistic simulations based on nearly all extant interatomic potentials and density functional theory (DFT) calculations of W-Re/Ta alloys. In binary W-TMs alloys, DFT calculations show that χ is related to the valence electron concentration at low to moderate solute concentrations, and can be controlled via alloying. χ can be quantitatively and efficiently predicted via rapid, low-cost DFT calculations for any BCC metal alloys, providing a robust, easily applied tool for the design of ductile and tough BCC alloys.

Machine learning assisted prediction of the Young’s modulus of compositionally complex alloys

We identify compositionally complex alloys (CCAs) that offer exceptional mechanical properties for elevated temperature applications by employing machine learning (ML) in conjunction with rapid synthesis and testing of alloys for validation to accelerate alloy design. The advantages of this approach are scalability, rapidity, and reasonably accurate predictions. ML tools were implemented to predict Young’s modulus of refractory-based CCAs by employing different ML models. Our results, in conjunction with experimental validation, suggest that average valence electron concentration, the difference in atomic radius, a geometrical parameter λ and melting temperature of the alloys are the key features that determine the Young’s modulus of CCAs and refractory-based CCAs. The Gradient Boosting model provided the best predictive capabilities (mean absolute error of 6.15 GPa) among the models studied. Our approach integrates high-quality validation data from experiments, literature data for training machine-learning models, and feature selection based on physical insights. It opens a new avenue to optimize the desired materials property for different engineering applications.

Effective Design of Cr-Co-Ni-Ta Eutectic Medium Entropy Alloys with High Compressive Properties Using Combined CALPHAD and Experimental Approaches

Alloy design of Cr-Co-Ni-Ta eutectic medium entropy alloys (EMEAs) was performed through a CALPHAD method coupled with experimental study, with the aim to attain high phase stability as well as excellent mechanical properties. Based on calculated pseudo-binary diagram, CrCoNiTax (x = 0.1, 0.3, 0.4, 0.5, 0.7) medium entropy alloys were investigated. Two phases, FCC solid solution and Laves phase, were identified in the alloys. With increasing Ta content, the volume fraction of hard and brittle Laves phase increased, microstructure changed from hypoeutectic (Ta0.1, Ta0.3) to eutectic (Ta0.4) and then to hypereutectic (Ta0.5, Ta0.7). The stability of phases was assessed by considering the thermodynamic parameter Ω and valence electron concentration (VEC). The eutectic phases become stable when 1.42 < Ω < 0.74 and 7.5 < VEC < 8.25. In addition, based on nanoindentation, the results indicated that solid solution strengthening in γ phase was significantly enhanced, eutectic phase in CrCoNiTa0.4 EMEA was found to process the highest microhardness and elastic modulus. Finally, the hardness of alloys was positively correlated with the content of Ta and the plastic strain of alloys obviously decreased, while the compression strength firstly increased and then decreased. CrCoNiTa0.4 was the most promising alloy with the highest compression strength (2502 MPa) and high plastic strain (20.6%).