Related papers: Enhancing Magnetocaloric Material Discovery: A Mac…
There currently exist no quantitative methods to determine the appropriate conditions for solid-state synthesis. This not only hinders the experimental realization of novel materials but also complicates the interpretation and understanding…
The development of magnetic refrigerators that operate at room temperature without the use of environmentally harmful substances represents a significant advancement in eco-friendly technology. These refrigerators employ the magnetocaloric…
We explore machine learning techniques for predicting Curie temperatures of magnetic materials using the NEMAD database. By augmenting the dataset with composition-based and domain-aware descriptors, we evaluate the performance of several…
Prediction of critical temperature $(T_c)$ of a superconductor remains a significant challenge in condensed matter physics. While the BCS theory explains superconductivity in conventional superconductors, there is no framework to predict…
Cost-effective materials are essential for large-scale deployment. The emerging magnetocaloric hydrogen liquefaction technology could transform the liquid hydrogen industry due to its potential in achieving higher efficiency. Most studies…
The search for energy-efficient and environmentally friendly cooling technologies is a key driver for the development of magnetic refrigeration based on the magnetocaloric effect (MCE). This phenomenon arises from the interplay between…
We perform a computational screening for two-dimensional magnetic materials based on experimental bulk compounds present in the Inorganic Crystal Structure Database and Crystallography Open Database. A recently proposed geometric descriptor…
The advent of caloric materials for magnetocaloric, elastocaloric, and electrocaloric cooling is changing the landscape of solid state cooling technologies with potentials for high-efficiency and environmentally-friendly residential and…
Magnetocaloric materials have received significant attention of research community as they can minimize the use of harmful gases (CFCs, HFCs) and render eco-friendly refrigeration. Heusler alloys (Ni2MnGa) are known for their magnetocaloric…
Metal-insulator transition (MIT) materials are a useful platform for emerging microelectronic, optoelectronic, and neuromorphic devices, but their discovery is hindered by the high computational cost of electronic structure modeling, the…
Nuclear materials are often demanded to function for extended time in extreme environments, including high radiation fluxes and transmutation, high temperature and temperature gradients, stresses, and corrosive coolants. They also have a…
Melting is a high temperature process that requires extensive sampling of configuration space, thus making melting temperature prediction computationally very expensive and challenging. Over the past few years, I have built two methods to…
Superconductivity is a remarkable phenomenon in condensed matter physics, which comprises a fascinating array of properties expected to revolutionize energy-related technologies and pertinent fundamental research. However, the field faces…
Recent progress on caloric effects are reviewed. The application of external stimuli such as magnetic field, hydrostatic pressure, uniaxial stress and electric field give rise respectively to magnetocaloric, barocaloric, elastocaloric and…
This study presents a machine learning approach to predict the Curie temperature in binary alloys, specifically focusing on the Fe-Pt, Fe-Ni, Fe-Pd, and Co-Pt compounds within a concentration range of 10 to 90 atomic percent. The optimal…
The calls to minimize greenhouse gas emissions and the demands for higher energy efficiency continue to drive the research worldwide in alternative cooling and refrigeration technologies. The field of caloric cooling has undergone a series…
We use a generative neural network model to create thousands of new, one-dimensional materials. The model is trained using 508 stable one-dimensional materials from the Computational 1D Materials Database (C1DB) database. More than 500 of…
Effective data center cooling is crucial for reliable operation; however, cooling systems often exhibit inefficiencies that result in excessive energy consumption. This paper presents a three-stage, physics-guided machine learning framework…
In the framework of solid-state cooling technology, four kinds of caloric effects, magnetocaloric (MC), electrocaloric (EC), barocaloric (BC) and elastocaloric (eC) effects, are compared in view of environment discussion and application…
Data-driven methods, in particular machine learning, can help to speed up the discovery of new materials by finding hidden patterns in existing data and using them to identify promising candidate materials. In the case of superconductors,…