相关论文: Identification of High-Dielectric Constant Compoun…

Hexagonal boron nitride (h-BN) is a critical material for 2D electronic devices for graphene and has attracted a considerable amount of attention owing to its structural similarity and semiconducting property. However, modifying its…

The large-scale search for high-performing candidate 2D materials is limited to calculating a few simple descriptors, usually with first-principles density functional theory calculations. In this work, we alleviate this issue by extending…

We employed a machine-learning assisted approach to search for superconducting hydrides under ambient pressure within an extensive dataset comprising over 150 000 compounds. Our investigation yielded around 50 systems with transition…

We perform a large scale study of conventional superconducting materials using a machine-learning accelerated high-throughput workflow. We start by creating a comprehensive dataset of around 7000 electron-phonon calculations performed with…

Utilizations of silicon-based luminescent devices are restricted by the indirect-gap nature of diamond silicon. In this study, the high-throughput method is employed to expedite discoveries of direct-gap silicon crystals. The machine…

Candidates compounds for new thermoelectric and superconducting materials, which have narrow band gap and flat bands near band edges, were exhaustively searched by a high-throughput first-principles calculation from an inorganic materials…

Flexoelectricity is a form of electromechanical coupling that has recently emerged because, unlike piezoelectricity, it is theoretically possible in any dielectric material. Two-dimensional (2D) materials have also garnered significant…

Regression machine learning is widely applied to predict various materials. However, insufficient materials data usually leads to a poor performance. Here, we develop a new voting data-driven method that could generally improve the…

The band structure and electronic properties in a series of vinylene-linked heterocyclic conducting polymers are investigated using density functional theory (DFT). In order to accurately calculate electronic band gaps, we utilize hybrid…

We present a computational framework that integrates machine learning with high-throughput \textit{ab initio} calculations to screen over 2.8 million compounds for metallic transport. We identify several intermetallic candidates with…

Nodal semimetals, materials systems with nodal-point or -line Fermi surfaces, are much sought after for their novel transport and topological properties. Identification of experimental materials candidates, however, has mainly relied on…

We have measured dielectric properties of Ca$_{1+x}$Cu$_{3-x}$Ti$_4$O$_{12}$ ($x$ = 0, 0.1, 0.5, 1, 1.5, 2, 2.9 and 3), and have found that Ca$_2$Cu$_2$Ti$_4$O$_{12}$ (a composite of CaCu$_3$Ti$_4$O$_{12}$ and CaTiO$_3$) exhibits a high…

In the present work we demonstrate that in addition to the well-known colossal-dielectric-constant material CaCu3Ti4O12 also various members of the series Ln2/3Cu3Ti4O12 with Ln = La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er and Tm, exhibit…

The electronic properties of two-dimensional (2D) materials depend sensitively on the underlying atomic arrangement down to the monolayer level. Here we present a novel strategy for the determination of the band gap and complex dielectric…

Dielectric breakdown has historically been of great interest from the perspectives of fundamental physics and electrical reliability. However, to date, the anisotropy in the dielectric breakdown has not been discussed. Here, we report an…

The predictive performance screening of novel compounds can significantly promote the discovery of efficient, cheap, and non-toxic thermoelectric materials. Large efforts to implement machine-learning techniques coupled to materials…

Discovering new superionic materials is essential for advancing solid-state batteries, which offer improved energy density and safety compared to the traditional lithium-ion batteries with liquid electrolytes. Conventional computational…

Multi-principal element alloys open large composition spaces for alloy development. The large compositional space necessitates rapid synthesis and characterization to identify promising materials, as well as predictive strategies for alloy…

The efficiency of active learning (AL) approaches to identify materials with desired properties relies on the knowledge of a few parameters describing the property. However, these parameters are unknown if the property is governed by a high…

Transition metal dichalcogenides (TMDs) attract significant attention due to their exceptional optical and excitonic properties. It was understood already in the 1960s, and recently rediscovered, that many TMDs possess high refractive index…