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

The evolution of cooperation in the public goods game on the scale-free community networks under multiple strategy updating rules

Social networks have a scale-free property and community structure, and many problems in life have the characteristic of public goods, such as resource shortage. Due to different preferences of individuals, there exist individuals who adopt heterogeneous strategies updating rules in the network. We investigate the evolution of cooperation in the scale-free community network with public goods games and the influence of multiple strategy updating rules. Here, two types of strategy updating rules are considered which are pairwise comparison rules and aspiration-driven rules. Numerical simulations are conducted and presented corresponding results. We find that community structure promotes the emergence of cooperation in public goods games. In the meantime, there is a "U" shape relationship between the frequency of cooperators and the proportion of the two strategy updating rules. With the variance in the proportion of the two strategy updating rules, pairwise comparison rules seem to be more sensitive. Compared with aspiration-driven rules, pairwise comparison rules play a more important role in promoting cooperation. Our work may be helpful to understand the evolution of cooperation in social networks.Comment: 6 figures, 11 page

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 NAH based on complex cepstrum method in a closed space

NAH method in non-free sound field reconstruction of the sound source will lead to considerable error. This paper proposes a method to reconstruct the sound source in a closed space. In the closed space, the total sound pressure is the sum of the source radiation pressure and the reflected sound pressure from medium interface. Reflections from medium interface is a convolution noise, rather than additive noise. In order to reconstruct a sound source in the closed space, in this paper we first adopt the complex cepstrum method to separate and radiation pressure and the reflected sound pressure, then filter them to reduction of reflected sound pressure, and then reconstruct the sound source. The simulation results show the correctness and effectiveness of the method

Thermal Hall effect in a van der Waals ferromagnet CrI3

CrI3 is a prototypical van der Waals ferromagnet with a magnetic honeycomb lattice. Previous inelastic neutron scattering studies have suggested topological nature of its magnetic excitations with a magnon gap at the Dirac points, which are anticipated to give rise to magnon thermal Hall effect. Here we report thermal transport properties of CrI3 and show that the long-sought thermal Hall signal anticipated for topological magnons is fairly small. In contrast, we find that CrI3 exhibits an appreciable anomalous thermal Hall signal at lower temperature which may arise from magnon-phonon hybridization or magnon-phonon scattering. These findings are anticipated to stimulate further neutron scattering studies on CrI3 single crystal, which can shed light not only on the intrinsic nature of magnetic excitations but also on the magnon-phonon interaction

Residual Film Pollution in the Eighth Division of the Xinjiang Production and Construction Corps

This study investigated the residual film content and distribution at different soil depths in the Eighth Division of the Xinjiang Production and Construction Corps. Before spring plowing in 2019, representative fields in four areas (Anjihai, Shihezi suburbs, Mosuowan and Xiayedi) were selected for residual film collection. The average content of residual film in the Eighth Division was 104 kg/ha. The residual film content in the four areas decreased in the order Anjihai ＞ Shihezi suburbs ＞ Mosuowan ＞ Xiayedi. The average amount of residual film collected from cotton fields was greater than that from corn fields. Residual film content in the cotton field at soil depths of 0～10 and 10～30 cm was higher than that in the corn field, whereas the residual film content at a 30–50 cm soil depth in the corn field was higher than that in the cotton field. The results showed that farmers do not consider the long-term benefits, the high cost and short time of recycling, and the easy recycling of surface residual film. The shallow that the higher content of residual film, the less water in the soil of cotton. The same time, the results showed that the quantity of residual film in cotton field had greater influence on cotton quality

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