Related papers: High-Throughput DFT-Based Discovery of Next Genera…

Van der Waals heterostructures (vdWHs) allow the assembly of high-crystalline two-dimensional (2D) materials in order to explore dimensionality effects in strongly correlated systems and the emergence of potential new physical scenarios. In…

We report the development of a combined machine-learning and high-throughput density functional theory (DFT) framework to accelerate the search for new ferroelectric materials. The framework can predict potential ferroelectric compounds…

Computational virtual high-throughput screening (VHTS) with density functional theory (DFT) and machine-learning (ML)-acceleration is essential in rapid materials discovery. By necessity, efficient DFT-based workflows are carried out with a…

We predict the possibility of realizing room-temperature superconductivity in different 2D domains within the ceramic high-Tc cuprates at ambient pressure and experimentally confirm this prediction of 2D room-temperature superconductivity…

Wiring systems powered by high-efficient superconductors have long been a dream of scientists, but researchers have faced practical challenges such as finding flexible materials. Here we report superconductivity in Nb2PdxS5-delta fibers…

Higher superconducting critical temperature and large-area epsilon-near-zero interfaces are two long-standing goals of Condensed Matter Physics and Optics. Motivated by the recent advancements of experimental interests on metallic…

Classical density functional theory (cDFT) provides a systematic approach to predict the structure and thermodynamic properties of chemical systems through the single-molecule density profiles. Whereas the statistical-mechanical framework…

The discovery of superconductivity in 3$d$-transition metal compounds with strong magnetism is interesting but rare. Especially for Mn-based compounds, there exist only very limited materials that show superconductivity. Here, we report the…

The marriage of density functional theory (DFT) and deep learning methods has the potential to revolutionize modern computational materials science. Here we develop a deep neural network approach to represent DFT Hamiltonian (DeepH) of…

Two-dimensional (2D) superconductors have attracted great attention in recent years due to the possibility of new phenomena in lower dimensions. With many bulk transition metal carbides being well-known conventional superconductors, here we…

Heusler compounds have always attracted a great deal of attention from researchers thanks to a wealth of interesting properties for technological applications. They are intermetallic ductile compounds, and some of them have been found to be…

Approximate density functional theory (DFT) has become indispensable owing to its cost-accuracy trade-off in comparison to more computationally demanding but accurate correlated wavefunction theory. To date, however, no single density…

Thanks to their unique properties single-layer 2-D materials appear as excellent candidates to extend Moore's scaling law beyond the currently manufactured silicon FinFETs. However, the known 2-D semiconducting components, essentially…

We have studied the optical conductivity of two-dimensional (2D) semiconducting transition metal dichalcogenides (STMDC) using ab-initio density functional theory (DFT). We find that this class of materials presents large optical response…

We demonstrate that a high-dimensional neural network potential (HDNNP) can predict the lattice thermal conductivity of semiconducting materials with an accuracy comparable to that of density functional theory (DFT) calculation. After a…

Real-time time-dependent density functional theory (TDDFT) is widely considered to be the most accurate available method for calculating electronic stopping powers from first principles, but there have been relatively few assessments of the…

The expansiveness of compositional phase space is too vast to fully search using current theoretical tools for many emergent problems in condensed matter physics. The reliance on a deep chemical understanding is one method to identify local…

A DFT study of the synthesized MAX phase Zr2SeC has been carried out for the first time to explore its physical properties for possible applications in many sectors. The studied properties are compared with prior known MAX phase Zr2SC. The…

Understanding the roles of disorder and superconducting phase fluctuation in superconductivity has been a long-standing challenge. For example, while the phase fluctuation is expected to destroy the superconductivity of intrinsically…

Since the discovery of superconductivity at 200 K in H3S [1] similar or higher transition temperatures, Tcs, have been reported for various hydrogen-rich compounds under ultra-high pressures [2]. Superconductivity was experimentally proved…