In recent years, high entropy alloys (HEAs) receive wide attention due to its unique alloy design concept and outstanding properties. This review presents a general overview of HEAs as a potential candidate for high‐temperature applications. The need for the profound research on the high‐temperature properties of HEAs is highlighted.

High-Entropy Alloys: Potential Candidates for High-Temperature Applications – An Overview

Deposition of FeCoNiCrMn high entropy alloy (HEA) coating via cold spraying

High-entropy alloys (HEAs) are a new generation of materials that exhibit unique characteristics and properties, and are demonstrating potential in the form of thermal spray coatings for demanding environments. The use of HEAs as feedstock for coating processes has advanced due to reports of their exceptional properties in both bulk and coating forms. Emerging reports of thermal sprayed HEA coatings outperforming conventional materials have accelerated further exploration of this field. This early-stage review discusses the outcomes of combining thermal spray and HEAs. Various synthesis routes adopted for HEA feedstock preparation and their properties are discussed, with reference to the requirements of thermal spray processing. The HEA feedstock is then compared and correlated with coating microstructure and phase composition as a function of the thermal spray processing route. Subsequently, the mechanical behavior of thermal spray HEA coatings is summarized in terms of porosity, hardness, and tribological properties, along with their oxidation and electrochemical properties, followed by their potential applications. The thermal spray methods are contrasted against laser cladding and surface alloying techniques for synthesizing thick HEA coatings. Furthermore, HEAs that have displayed excellent properties via alternative processing routes, but have not been explored within the framework of thermal spray, are recommended.

/pdf/thermal-spray-high-entropy-alloy-coatings-a-review-26qv6xu62g.pdf

Thermal Spray High-Entropy Alloy Coatings: A Review

Properties of atomized AlCoCrFeNi high-entropy alloy powders and their phase-adjustable coatings prepared via plasma spray process

A Review on High Entropy Alloys Coatings: Fabrication Processes and Property Assessment

Thermal sprayed high-entropy NiCo0.6Fe0.2Cr1.5SiAlTi0.2 coating with improved mechanical properties and oxidation resistance

De novo drug design with desired biological activities is crucial for developing novel therapeutics for patients. The drug development process is time- and resource-consuming, and it has a low probability of success. Recent advances in machine learning and deep learning technology have reduced the time and cost of the discovery process and therefore, improved pharmaceutical research and development. In this paper, we explore the combination of two rapidly developing fields with lead candidate discovery in the drug development process. First, artificial intelligence has already been demonstrated to successfully accelerate conventional drug design approaches. Second, quantum computing has demonstrated promising potential in different applications, such as quantum chemistry, combinatorial optimizations, and machine learning. This article explores hybrid quantum-classical generative adversarial networks (GAN) for small molecule discovery. We substituted each element of GAN with a variational quantum circuit (VQC) and demonstrated the quantum advantages in the small drug discovery. Utilizing a VQC in the noise generator of a GAN to generate small molecules achieves better physicochemical properties and performance in the goal-directed benchmark than the classical counterpart. Moreover, we demonstrate the potential of a VQC with only tens of learnable parameters in the generator of GAN to generate small molecules. We also demonstrate the quantum advantage of a VQC in the discriminator of GAN. In this hybrid model, the number of learnable parameters is significantly less than the classical ones, and it can still generate valid molecules. The hybrid model with only tens of training parameters in the quantum discriminator outperforms the MLP-based one in terms of both generated molecule properties and the achieved KL divergence. However, the hybrid quantum-classical GANs still face challenges in generating unique and valid molecules compared to their classical counterparts.

https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.3c00562

Ya-Chu Yang

Papers

Thermal sprayed high-entropy NiCo0.6Fe0.2Cr1.5SiAlTi0.2 coating with improved mechanical properties and oxidation resistance

Exploring the Advantages of Quantum Generative Adversarial Networks in Generative Chemistry