Theory-guided design of high-entropy alloys with enhanced strength-ductility synergy.

Metallic alloys have played essential roles in human civilization due to their balanced strength and ductility. Metastable phases and twins have been introduced to overcome the strength-ductility tradeoff in face-centered cubic (FCC) high-entropy alloys (HEAs). However, there is still a lack of quantifiable mechanisms to predict good combinations of the two mechanical properties. Here we propose a possible mechanism based on the parameter κ, the ratio of short-ranged interactions between closed-pack planes. It promotes the formation of various nanoscale stacking sequences and enhances the work-hardening ability of the alloys. Guided by the theory, we successfully designed HEAs with enhanced strength and ductility compared with other extensively studied CoCrNi-based systems. Our results not only offer a physical picture of the strengthening effects but can also be used as a practical design principle to enhance the strength-ductility synergy in HEAs.

Toward the design of ultrahigh-entropy alloys via mining six million texts.

It has long been a norm that researchers extract knowledge from literature to design materials. However, the avalanche of publications makes the norm challenging to follow. Text mining (TM) is efficient in extracting information from corpora. Still, it cannot discover materials not present in the corpora, hindering its broader applications in exploring novel materials, such as high-entropy alloys (HEAs). Here we introduce a concept of "context similarity" for selecting chemical elements for HEAs, based on TM models that analyze the abstracts of 6.4 million papers. The method captures the similarity of chemical elements in the context used by scientists. It overcomes the limitations of TM and identifies the Cantor and Senkov HEAs. We demonstrate its screening capability for six- and seven-component lightweight HEAs by finding nearly 500 promising alloys out of 2.6 million candidates. The method thus brings an approach to the development of ultrahigh-entropy alloys and multicomponent materials.

Decoupling between Shockley partials and stacking faults strengthens multiprincipal element alloys.

Mechanical properties are fundamental to structural materials, where dislocations play a decisive role in describing their mechanical behavior. Although the high-yield stresses of multiprincipal element alloys (MPEAs) have received extensive attention in the last decade, the relation between their mechanistic origins remains elusive. Our multiscale study of density functional theory, atomistic simulations, and high-resolution microscopy shows that the excellent mechanical properties of MPEAs have diverse origins. The strengthening effects through Shockley partials and stacking faults can be decoupled in MPEAs, breaking the conventional wisdom that low stacking fault energies are coupled with wide partial dislocations. This study clarifies the mechanistic origins for the strengthening effects, laying the foundation for physics-informed predictive models for materials design.

Machine-Learning Microstructure for Inverse Material Design.

Metallurgy and material design have thousands of years' history and have played a critical role in the civilization process of humankind. The traditional trial-and-error method has been unprecedentedly challenged in the modern era when the number of components and phases in novel alloys keeps increasing, with high-entropy alloys as the representative. New opportunities emerge for alloy design in the artificial intelligence era. Here, a successful machine-learning (ML) method has been developed to identify the microstructure images with eye-challenging morphology for a number of martensitic and ferritic steels. Assisted by it, a new neural-network method is proposed for the inverse design of alloys with 20 components, which can accelerate the design process based on microstructure. The method is also readily applied to other material systems given sufficient microstructure images. This work lays the foundation for inverse alloy design based on microstructure images with extremely similar features.

High-throughput design of high-performance lightweight high-entropy alloys.

Developing affordable and light high-temperature materials alternative to Ni-base superalloys has significantly increased the efforts in designing advanced ferritic superalloys. However, currently developed ferritic superalloys still exhibit low high-temperature strengths, which limits their usage. Here we use a CALPHAD-based high-throughput computational method to design light, strong, and low-cost high-entropy alloys for elevated-temperature applications. Through the high-throughput screening, precipitation-strengthened lightweight high-entropy alloys are discovered from thousands of initial compositions, which exhibit enhanced strengths compared to other counterparts at room and elevated temperatures. The experimental and theoretical understanding of both successful and failed cases in their strengthening mechanisms and order-disorder transitions further improves the accuracy of the thermodynamic database of the discovered alloy system. This study shows that integrating high-throughput screening, multiscale modeling, and experimental validation proves to be efficient and useful in accelerating the discovery of advanced precipitation-strengthened structural materials tuned by the high-entropy alloy concept.

Hidden Effects of Negative Stacking Fault Energies in Complex Concentrated Alloys.

Negative stacking fault energies (SFEs) are found in face-centered cubic high-entropy alloys with excellent mechanical properties, especially at low temperatures. Their roles remain elusive due to the lack of in situ observation of nanoscale deformation. Here, the polymorphism of Shockley partials is fully explored, assisted by a new method. We show negative SFEs result in novel partial pairs as if they were in hexagonal close-packed alloys. The associated yield stresses are much higher than those for other mechanisms at low temperatures. This generalizes the physical picture for all negative-SFE alloys.

Neural network-based order parameter for phase transitions and its applications in high-entropy alloys.

Phase transition is one of the most important phenomena in nature and plays a central role in materials design. All phase transitions are characterized by suitable order parameters, including the order-disorder phase transition. However, finding a representative order parameter for complex systems is non-trivial, such as for high-entropy alloys. Given the strength of dimensionality reduction of a variational autoencoder (VAE), we introduce a VAE-based order parameter. We propose that the Manhattan distance in the VAE latent space can serve as a generic order parameter for order-disorder phase transitions. The physical properties of our order parameter are quantitatively interpreted and demonstrated by multiple refractory high-entropy alloys. Using this order parameter, a generally applicable alloy design concept is proposed by mimicking the natural mixing process of elements. Our physically interpretable VAE-based order parameter provides a computational technique for understanding chemical ordering in alloys, which can facilitate the development of rational alloy design strategies.

The local strain distribution in bilayer materials: a multiscale study.

Recent studies show that small geometric changes can result in dramatic changes in physical properties and need to be carefully evaluated. In this regard, we calculate the distribution of local strains in bilayer graphene and two configurations of hexagonal BN (h-BN), which is different from previous studies that focus on homogeneous strains in such materials. We consider a mismatch of one lattice parameter and calculate how strain distributes without external stresses. This problem is equivalent to finding the core structure of a type of dislocation profuse in structural materials. The strain distribution is transformed into the core distribution of a dislocation, which is calculated using a new formulation proposed by us. The new formulation finds new lower-energy states for the 2D materials. Our results show that the strain of one-lattice mismatch in bilayer graphene forms two Lorentz peaks with half widths of 117b-120b (edge component) and 67b-80b (screw component), where b is the lattice constant. The case for bilayer h-BN is slightly more complicated but the results are also presented. Our analytic solutions, which are based on the new formulation with more freedom in structural relaxation, provide the basis for the next-step study of their electronic properties.

First-principles study of order-disorder transitions in multicomponent solid-solution alloys.

In this review, we will focus on the recent development of the order-disorder transition in metallic materials. The past decades have witnessed fast development in the first-principles methodologies and their applications to ordering transitions in multi-component alloys, particularly the high-entropy alloys. The driving force for the proceedings comes from (i) the advance of algorithms and increasingly cheaper hardware, and also (ii) the great passion to model alloys with increasing number of components. The review starts with a brief introduction of the history for the ordering transitions. More detailed scientific proceedings prior to the 1970s had been well summarized in Krivoglaz and Smirnov (1965 The Theory of Order-Disorder in Alloys (New York: Elsevier)) and Stoloff and Davies (1968 Prog. Mater. Sci. 13 1-84). In the second part, the methods to study the ordering transitions, primarily on the theoretic methods are introduced. These will include (i) KKR-CPA method and supercell methods for energetic calculations; and (ii) thermodynamic and statistical methods to compute the transition temperatures. The third part will focus on representative applications in alloys, including our own work and many others. This part supplies the primary information of this review to the readers. The fourth part will summarize the connections between ordering transitions and broader physical properties (e.g. the mechanical properties). In the last part, some concluding remarks and perspectives will be given.