Related papers: Computational screening of magnetocaloric alloys
This study examines the application of machine learning algorithms, specifically the Random Forest regression model, to optimize the magnetocaloric effect in all-d-metal Heusler alloys. The model was trained using descriptors related to the…
The study of alloys using computational methods has been a difficult task due to the usually unknown stoichiometry and local atomic ordering of the different structures experimentally. In order to combat this, first-principles methods have…
In the dynamic and rapidly advancing battery field, alloy anode materials are a focal point due to their superior electrochemical performance. Traditional screening methods are inefficient and time-consuming. Our research introduces a…
The changes in the electronic properties of the substitutionally disordered MgC(Ni_{1-x}T_{x})_{3} (T=Fe, Co or Cu) alloys are studied using the atomic sphere formulation of the Korringa-Kohn-Rostoker coherent-potential approximation method…
Multi principal element alloys (MPEAs) comprise a unique class of metal alloys. MPEAs have been demonstrated to possess several exceptional properties, including, as most relevant to the present study, a high corrosion resistance. In the…
Understanding structure-property relationships in complex materials requires integrating complementary measurements across multiple length scales. Here we propose an interpretable "multimodal" machine learning framework that unifies…
The large magnetocaloric effect (MCE), which accompanies the first order ferromagnetic/anti-ferromagnetic transition in CsCl-ordered Fe-Rh alloys, has been investigated by measurements in slowly cycled magnetic fields of up to 2 T in…
Modern advances in generating ultrabright electron beams have unlocked unprecedented experimental advances based on synchrotron radiation. Current challenges lie in improving the quality of electron sources with novel photocathode materials…
Recent work shows that density functional theory calculations accurately describe materials exhibiting turbostratic disorder between layers of incommensurate constituents. These calculations approximate one of the constituents as a finite…
Atomic-resolution imaging with scanning transmission electron microscopy is a powerful tool for characterizing the nanoscale structure of materials, in particular features such as defects, local strains, and symmetry-breaking distortions.…
Materials informatics offers a promising pathway towards rational materials design, replacing the current trial-and-error approach and accelerating the development of new functional materials. Through the use of sophisticated data analysis…
Proton-conducting solid acids could enable water-free operation of high-temperature fuel cells. However, systematic materials screening has, hitherto, been computationally prohibitive. Here, we introduce a two-stage high-throughput…
Materials exhibiting a substitutional disorder such as multicomponent alloys and mixed metal oxides/oxyfluorides are of great importance in many scientific and technological sectors. Disordered materials constitute an overwhelmingly large…
Crystalline materials, with symmetrical and periodic structures, exhibit a wide spectrum of properties and have been widely used in numerous applications across electronics, energy, and beyond. For crystalline materials discovery,…
Modern high-resolution microscopes, such as the scanning tunneling microscope, are commonly used to study specimens that have dense and aperiodic spatial structure. Extracting meaningful information from images obtained from such…
While deep learning has seen many recent applications to drug discovery, most have focused on predicting activity or toxicity directly from chemical structure. Phenotypic changes exhibited in cellular images are also indications of the…
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…
Efficient characterization of surface compositions across high-dimensional materials spaces is critical for accelerating the discovery of surface-dominated functional materials. While X-ray photoelectron spectroscopy allows detailed surface…
Magnetic cooling has the potential to replace conventional gas compression refrigeration. Materials such as La(Fe,Si)$_{13}$ exhibit a sizeable first-order magnetocaloric effect, and it is possible to tailor the phase transition towards…
Discovering nonlinear optical (NLO) materials with strong shift current response, particularly in the infrared (IR) regime, is essential for next-generation optoelectronics yet remains highly challenging in both experiments and theory,…