Related papers: Enhancing Magnetocaloric Material Discovery: A Mac…
Materials with higher operating temperatures than today's state of the art can improve system performance in several applications and enable new technologies. Under most scenarios, a protective oxide scale with high melting temperatures and…
The existence and feasibility of the multicaloric, polycrystalline material 0.8Pb(Fe1/2Nb1/2)O3-0.2Pb(Mg1/2W1/2)O3, exhibiting magnetocaloric and electrocaloric properties, are demonstrated. Both the electrocaloric and magnetocaloric…
We introduce a first-principles method for predicting the magnetothermal properties of solid-state materials, which we call Sampled Effective Local Field Estimation. This approach achieves over two orders of magnitude improvement in sample…
We discover many new crystalline solid materials with fast single crystal Li ion conductivity at room temperature, discovered through density functional theory simulations guided by machine learning-based methods. The discovery of new solid…
Solid-state cooling is an environmentally friendly and highly scalable technology that may solve most of the problems associated with current refrigerant methods. Solid-state cooling consists of applying external fields on caloric…
The magnetocaloric effect (MCE) provides a promising foundation for the development of solid-state refrigeration technologies that could replace conventional gas compression-based cooling systems. Current research efforts primarily focus on…
In this study, we evaluate several classifiers and focus on selecting a minimal set of appropriate material features. Our objective is to propose and discuss general strategies for reducing the number of descriptors required for material…
Increased demand for high-performance permanent magnets in the electric vehicle and wind turbine industries has prompted the search for cost-effective alternatives.Discovering new magnetic materials with the desired intrinsic and extrinsic…
Various techniques can be employed to determine the temperature of magnetic transformation, whether it be the Curie or Neel temperature. The standard procedure typically involves creating alloys with defined compositions and performing…
Much research in recent years has focused on using empirical machine learning approaches to extract useful insights on the structure-property relationships of superconductor material. Notably, these approaches are bringing extreme benefits…
One of the main goals and challenges of materials discovery is to find the best candidates for each interest property or application. Machine learning rises in this context to efficiently optimize this search, exploring the immense…
Safe and high-density storage of hydrogen, for a clean-fuel economy, can be realized by hydride-forming materials, but these materials should be able to store hydrogen at room temperature. Some high-entropy alloys (HEAs) have recently been…
Magnetic refrigeration at room temperature is of great interest due to a long-term goal of making refrigeration more energy-efficient, less noisy and free of any environmentally hostile materials. A refrigerator utilizing an active magnetic…
Metallic glasses are a promising class of materials celebrated for their exceptional thermal and mechanical properties. However, accurately predicting and understanding the melting temperature (T_m) and glass transition temperature (T_g)…
Thermal management in the hyper-scale cloud data centers is a critical problem. Increased host temperature creates hotspots which significantly increases cooling cost and affects reliability. Accurate prediction of host temperature is…
First-principles computations are the driving force behind numerous discoveries of hydride-based superconductors, mostly at high pressures, during the last decade. Machine-learning (ML) approaches can further accelerate the future…
Accelerating the discovery of high-performance materials remains a central challenge across energy, electronics, and aerospace technologies, where traditional workflows depend heavily on expert intuition and computationally expensive…
Traditional materials discovery approaches - relying primarily on laborious experiments - have controlled the pace of technology. Instead, computational approaches offer an accelerated path: high-throughput exploration and characterization…
Active learning (AL) can drastically accelerate materials discovery; its power has been shown in various classes of materials and target properties. Prior efforts have used machine learning models for the optimal selection of physical…
Magnetic refrigeration based on the magnetocaloric effect (MCE) of materials is a potential technique that has prominet advantages over the currently used gas compression-expansion technique in the sense of its high efficiency and…