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.

Dataset for machine learning of microstructures for 9% Cr steels.

The microstructure of steel greatly influences the mechanical properties. For 9 wt% Cr steels, which are widely used in the power generation industry, the steels have a ferritic and martensitic microstructure which can be altered by heat treating and chemical composition variations. Fully martensitic steels typically having high yield strengths but low ductility. Tempering can reduce the amount of martensite in the steel lowering the yield strength but increasing the ductility of the alloy. Alloying can alter the time required for a martensitic transformation. In authors' previously published research, the authors used machine learning methodology to predict room temperature tensile properties from scanning electron microscopy (SEM) images of the initial steel microstructures from a wide range of steel compositions. This data-in-brief supplies the raw image files and the associated tensile properties for the authors' previously published research utilized to predict tensile properties of steels [1].

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.

Superior High-Temperature Strength in a Supersaturated Refractory High-Entropy Alloy.

Refractory high-entropy alloys (RHEAs) show promising applications at high temperatures. However, achieving high strengths at elevated temperatures above 1173K is still challenging due to heat softening. Using intrinsic material characteristics as the alloy-design principles, a single-phase body-centered-cubic (BCC) CrMoNbV RHEA with high-temperature strengths (beyond 1000 MPa at 1273 K) is designed, superior to other reported RHEAs as well as conventional superalloys. The origin of the high-temperature strength is revealed by in situ neutron scattering, transmission-electron microscopy, and first-principles calculations. The CrMoNbV's elevated-temperature strength retention up to 1273 K arises from its large atomic-size and elastic-modulus mismatches, the insensitive temperature dependence of elastic constants, and the dominance of non-screw character dislocations caused by the strong solute pinning, which makes the solid-solution strengthening pronounced. The alloy-design principles and the insights in this study pave the way to design RHEAs with outstanding high-temperature strength.

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.

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.

Lattice-Distortion-Enhanced Yield Strength in a Refractory High-Entropy Alloy.

Severe distortion is one of the four core effects in single-phase high-entropy alloys (HEAs) and contributes significantly to the yield strength. However, the connection between the atomic-scale lattice distortion and macro-scale mechanical properties through experimental verification has yet to be fully achieved, owing to two critical challenges: 1) the difficulty in the development of homogeneous single-phase solid-solution HEAs and 2) the ambiguity in describing the lattice distortion and related measurements and calculations. A single-phase body-centered-cubic (BCC) refractory HEA, NbTaTiVZr, using thermodynamic modeling coupled with experimental verifications, is developed. Compared to the previously developed single-phase NbTaTiV HEA, the NbTaTiVZr HEA shows a higher yield strength and comparable plasticity. The increase in yield strength is systematically and quantitatively studied in terms of lattice distortion using a theoretical model, first-principles calculations, synchrotron X-ray/neutron diffraction, atom-probe tomography, and scanning transmission electron microscopy techniques. These results demonstrate that severe lattice distortion is a core factor for developing high strengths in refractory HEAs.

Temperature dependence of elastic and plastic deformation behavior of a refractory high-entropy alloy.

Single-phase solid-solution refractory high-entropy alloys (HEAs) show remarkable mechanical properties, such as their high yield strength and substantial softening resistance at elevated temperatures. Hence, the in-depth study of the deformation behavior for body-centered cubic (BCC) refractory HEAs is a critical issue to explore the uncovered/unique deformation mechanisms. We have investigated the elastic and plastic deformation behaviors of a single BCC NbTaTiV refractory HEA at elevated temperatures using integrated experimental efforts and theoretical calculations. The in situ neutron diffraction results reveal a temperature-dependent elastic anisotropic deformation behavior. The single-crystal elastic moduli and macroscopic Young's, shear, and bulk moduli were determined from the in situ neutron diffraction, showing great agreement with first-principles calculations, machine learning, and resonant ultrasound spectroscopy results. Furthermore, the edge dislocation-dominant plastic deformation behaviors, which are different from conventional BCC alloys, were quantitatively described by the Williamson-Hall plot profile modeling and high-angle annular dark-field scanning transmission electron microscopy.

A Multidisciplinary Discharge Timeout Checklist Improves Patient Education and Captures Discharge Process Errors.

OBJECTIVE: To design and implement a discharge timeout checklist, and to assess its effects on patients' understanding as well as the potential impact on preventable medical errors surrounding hospital discharges to home. METHODS: Based on the structure successfully used for surgical procedures and using the Model for Improvement framework, we designed a discharge checklist to review and assess patients' understanding of discharge medications, catheters, home care plans, follow-up, symptoms, and who to call with problems after discharge. In parallel, we developed a process of integrating the checklist into the discharge process after routine discharge procedures were completed. We used the checklists to assess patients' level of understanding and need for additional education as well as changes in discharge documentation; we also noted whether good catches of significant errors in the discharge process occurred. RESULTS: Over 6 months of study, 190 discharge timeouts out of 429 eligible discharges were completed. Additional education was provided in 53 of 190 discharge timeouts (27.8%), with 62% of this education being related to medications. Twenty-one (11.1%) discharge timeouts resulted in at least one change to the discharge documentation or a good catch. CONCLUSIONS: A multidisciplinary discharge timeout directly involving the patient can be effective in targeting additional areas for patient education and in potentially reducing preventable adverse events.

Deviation from high-entropy configurations in the atomic distributions of a multi-principal-element alloy.

The alloy-design strategy of combining multiple elements in near-equimolar ratios has shown great potential for producing exceptional engineering materials, often known as 'high-entropy alloys'. Understanding the elemental distribution, and, thus, the evolution of the configurational entropy during solidification, is undertaken in the present study using the Al1.3CoCrCuFeNi model alloy. Here we show that, even when the material undergoes elemental segregation, precipitation, chemical ordering and spinodal decomposition, a significant amount of disorder remains, due to the distributions of multiple elements in the major phases. The results suggest that the high-entropy alloy-design strategy may be applied to a wide range of complex materials, and should not be limited to the goal of creating single-phase solid solutions.

Treatment of a intracranial stenosis with a vulnerable plaque under proximal flow reversal with balloon angioplasty and stent placement.

Intracranial stenosis is associated with a high risk of ischemic events, even with medical treatment. Percutaneous transluminal angioplasty and stenting (PTAS) is an accepted intervention for addressing a moderate intracranial stenosis (≥50%) that is refractory to medical treatment. Distal emboli are seen in 34% to 70% of patients on MRI diffusion weighted images post PTAS. We present a unique case of an intracranial stenosis with a vulnerable plaque treated with PTAS under proximal flow reversal to decrease the risk of possible distal embolization.

Intelligence in Williams Syndrome is related to STX1A, which encodes a component of the presynaptic SNARE complex.

Although genetics is the most significant known determinant of human intelligence, specific gene contributions remain largely unknown. To accelerate understanding in this area, we have taken a new approach by studying the relationship between quantitative gene expression and intelligence in a cohort of 65 patients with Williams Syndrome (WS), a neurodevelopmental disorder caused by a 1.5 Mb deletion on chromosome 7q11.23. We find that variation in the transcript levels of the brain gene STX1A correlates significantly with intelligence in WS patients measured by principal component analysis (PCA) of standardized WAIS-R subtests, r = 0.40 (Pearson correlation, Bonferroni corrected p-value = 0.007), accounting for 15.6% of the cognitive variation. These results suggest that syntaxin 1A, a neuronal regulator of presynaptic vesicle release, may play a role in WS and be a component of the cellular pathway determining human intelligence.

The genetic architecture of Down syndrome phenotypes revealed by high-resolution analysis of human segmental trisomies.

Down syndrome (DS), or trisomy 21, is a common disorder associated with several complex clinical phenotypes. Although several hypotheses have been put forward, it is unclear as to whether particular gene loci on chromosome 21 (HSA21) are sufficient to cause DS and its associated features. Here we present a high-resolution genetic map of DS phenotypes based on an analysis of 30 subjects carrying rare segmental trisomies of various regions of HSA21. By using state-of-the-art genomics technologies we mapped segmental trisomies at exon-level resolution and identified discrete regions of 1.8-16.3 Mb likely to be involved in the development of 8 DS phenotypes, 4 of which are congenital malformations, including acute megakaryocytic leukemia, transient myeloproliferative disorder, Hirschsprung disease, duodenal stenosis, imperforate anus, severe mental retardation, DS-Alzheimer Disease, and DS-specific congenital heart disease (DSCHD). Our DS-phenotypic maps located DSCHD to a <2-Mb interval. Furthermore, the map enabled us to present evidence against the necessary involvement of other loci as well as specific hypotheses that have been put forward in relation to the etiology of DS-i.e., the presence of a single DS consensus region and the sufficiency of DSCR1 and DYRK1A, or APP, in causing several severe DS phenotypes. Our study demonstrates the value of combining advanced genomics with cohorts of rare patients for studying DS, a prototype for the role of copy-number variation in complex disease.