Machine Learning for High-entropy Alloys: Progress, Challenges and Opportunities

High-entropy alloys (HEAs) have attracted extensive interest due to their exceptional mechanical properties and the vast compositional space for new HEAs. However, understanding their novel physical mechanisms and then using these mechanisms to design new HEAs are confronted with their high-dimensional chemical complexity, which presents unique challenges to (i) the theoretical modeling that needs accurate atomic interactions for atomistic simulations and (ii) constructing reliable macro-scale models for high-throughput screening of vast amounts of candidate alloys. Machine learning (ML) sheds light on these problems with its capability to represent extremely complex relations. This review highlights the success and promising future of utilizing ML to overcome these challenges. We first introduce the basics of ML algorithms and application scenarios. We then summarize the state-of-the-art ML models describing atomic interactions and atomistic simulations of thermodynamic and mechanical properties. Special attention is paid to phase predictions, planar-defect calculations, and plastic deformation simulations. Next, we review ML models for macro-scale properties, such as lattice structures, phase formations, and mechanical properties. Examples of machine-learned phase-formation rules and order parameters are used to illustrate the workflow. Finally, we discuss the remaining challenges and present an outlook of research directions, including uncertainty quantification and ML-guided inverse materials design.Comment: This review paper has been accepted by Progress in Materials Scienc

A Review of Smart Materials in Tactile Actuators for Information Delivery

As the largest organ in the human body, the skin provides the important sensory channel for humans to receive external stimulations based on touch. By the information perceived through touch, people can feel and guess the properties of objects, like weight, temperature, textures, and motion, etc. In fact, those properties are nerve stimuli to our brain received by different kinds of receptors in the skin. Mechanical, electrical, and thermal stimuli can stimulate these receptors and cause different information to be conveyed through the nerves. Technologies for actuators to provide mechanical, electrical or thermal stimuli have been developed. These include static or vibrational actuation, electrostatic stimulation, focused ultrasound, and more. Smart materials, such as piezoelectric materials, carbon nanotubes, and shape memory alloys, play important roles in providing actuation for tactile sensation. This paper aims to review the background biological knowledge of human tactile sensing, to give an understanding of how we sense and interact with the world through the sense of touch, as well as the conventional and state-of-the-art technologies of tactile actuators for tactile feedback delivery

The Architecture and Performance Evaluation of iSCSI-Based United Storage Network Merging NAS and SAN

With the ever increasing volume of data in networks, the traditional storage architecture is greatly challenged; more and more people pay attention to network storage. Currently, the main technology of network storage is represented by NAS (Network Attached Storage) and SAN (Storage Area Network). They are different, but mutually complementary and used under different circumstances; however, both NAS and SAN may be needed in the same company. To reduce the TOC (total of cost), for easier implementation, etc., people hope to merge the two technologies. Additionally, the main internetworking technology of SAN is the Fibre Channel; however, the major obstacles are in its poor interoperability, lack of trained staff, high implementation costs, etc. To solve the above-mentioned issues, this paper creatively introduces a novel storage architecture called USN (United Storage Networks), which uses the iSCSI to build the storage network, and merges the NAS and SAN techniques supplying the virtues and overcoming the drawbacks of both, and provides both file I/O and block I/O service simultaneously

THE RELATIONSHIP BETWEEN HAMSTRING FLEXIBILITY AND MAXIMAL STRAINS OF HAMSTRING MUSCLE-TENDON UNITS IN SPRINTING: INDICATION TO HAMSTRING STRAIN INJURY

The purpose of this study was to determine the relationship between hamstring flexibility and maximal strain in sprinting. Ten male and 10 female reactional athletes participated this study. Hamstring flexibility, isokinetic strength data, three-dimensional kinematic data in a hamstring isokinetic test, and kinematic data in a sprinting test were collected for each participant. The optimal hamstring muscle lengths and maximal strains of hamstring muscle-tendon units in sprinting were determined for each participant. Maximal strains of hamstring muscle-tendon units were negatively correlated to hamstring flexibility. Maximal strains of biceps long head and semitendinosus muscle-tendon units were significantly greater than that of semimembranosus. Hamstring flexibility is a factor that significantly affect maximal strain of hamstring muscle-tendon units in sprinting

THE RELATIONSHIP BETWEEN OPTIMAL KNEE FLEXION ANGLE AND HAMST RING FLEXIBILITY: INDICATION FOR HAMSTRING STRAIN INJURY

The purpose of this study was to determine the relationships among hamstring flexibility, optimal knee flexion angle for maximal knee flexion moment, maximal knee flexion moment. Ten male and 10 female reactional athletes were tested for hamstring flexibility and isokinetic strength. The maximal knee flexion moment and the knee flexion angle corresponding to the maximal knee flexion moment were determined for each participant. Optimal knee flexion angle was a function of hamstring flexibility score and gender, but not of the hamstring strength. Optimal knee flexion angle and hamstring strength were not correlated. These results indicate that hamstring muscle optimal length is correlated to its flexibility, but not to its strength. Increased hamstring flexibility is correlated with increased muscle optimal length. Hamstring flexibility may be a risk factor for hamstring strain injury

A novel convergence enhancement method based on Online Dimension Reduction Optimization

Iterative steady-state solvers are widely used in computational fluid dynamics. Unfortunately, it is difficult to obtain steady-state solution for unstable problem caused by physical instability and numerical instability. Optimization is a better choice for solving unstable problem because steady-state solution is always the extreme point of optimization regardless of whether the problem is unstable or ill-conditioned, but it is difficult to solve partial differential equations (PDEs) due to too many optimization variables. In this study, we propose an Online Dimension Reduction Optimization (ODRO) method to enhance the convergence of the traditional iterative method to obtain the steady-state solution of unstable problem. This method performs proper orthogonal decomposition (POD) on the snapshots collected from a few iteration steps, optimizes PDE residual in the POD subspace to get a solution with lower residual, and then continues to iterate with the optimized solution as the initial value, repeating the above three steps until the residual converges. Several typical cases show that the proposed method can efficiently calculate the steady-state solution of unstable problem with both the high efficiency and robustness of the iterative method and the good convergence of the optimization method. In addition, this method is easy to implement in almost any iterative solver with minimal code modification