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The prediction of stable crystal structures is an important part of designing solid-state crystalline materials with desired properties. Recent advances in structural feature representations and generative neural networks promise the ability to efficiently create new stable structures to use for inverse design and to search for materials with tailored functionalities. 

Abstract This paper presents a roadmap to the application of AI techniques and big data (BD) for different modelling, design, monitoring, manufacturing and operation purposes of different superconducting applications. To help superconductivity researchers, engineers, and manufacturers understand the viability of using AI and BD techniques as future solutions for challenges in superconductivity, a series of short articles are presented to outline some of the potential applications and solutions. These potential futuristic routes and their materials/technologies are considered for a 10–20 yr time-frame. 

Abstract Motivated by properties-controlling potential of the strain, we investigate strain dependence of structure, electronic, and magnetic properties of Sr 2 IrO 4 using complementary theoretical tools: ab-initio calculations, analytical approaches (rigid octahedra picture, Slater-Koster integrals), and extended $$t-{{{\mathcal{J}}}}$$ t − J model. We find that strain affects both Ir-Ir distance and Ir-O-Ir angle, and the rigid octahedra picture is not relevant. Second, we find fundamentally different behavior for compressive and tensile strain. One remarkable feature is the formation of two subsets of bond- and orbital-dependent carriers, a compass-like model, under compression. This originates from the strain-induced renormalization of the Ir-O-Ir superexchange and O on-site energy. We also show that under compressive (tensile) strain, Fermi surface becomes highly dispersive (relatively flat). Already at a tensile strain of 1.5%, we observe spectral weight redistribution, with the low-energy band acquiring almost purely singlet character. These results can be directly compared with future experiments. 

Abstract The superconducting critical temperature T c of intercalated iron-selenide superconductor (Li,Fe)OHFeSe (FeSe11111) can be increased to 42 from 8 K of bulk FeSe. It shows remarkably similar electronic properties as the high- T c monolayer FeSe and provides a bulk counterpart to investigate the origin of enhanced superconductivity. Unraveling the nature of excitations is crucial for understanding the pairing mechanism in high- T c iron selenides. Here we use resonant inelastic x-ray scattering (RIXS) to investigate the excitations in FeSe11111. Our high-quality data exhibit several Raman-like excitations, which are dispersionless and isotropic in momentum transfer in both superconducting 28 K and 42 K samples. Using atomic multiplet calculations, we assign the low-energy ~0.3 and 0.7 eV Raman peaks as local e g  −  e g and e g  −  t 2 g orbital excitations. The intensity of these two features decreases with increasing temperature, suggesting a dominating contribution of the orbital fluctuations. Our results highlight the importance of the orbital degree of freedom for high- T c iron selenides.