Related papers: Accelerating Discovery of Metal-Insulator Transiti…
Transition-state searches are central to understanding reaction mechanisms, but the high computational cost of density-functional theory (DFT) limits their application in high-throughput catalyst and materials discovery. Machine-learned…
Magnetic topological insulators and semi-metals have a variety of properties that make them attractive for applications including spintronics and quantum computation, but very few high-quality candidate materials are known. In this work, we…
Metal Organic Frameworks (MOFs) are promising materials to help mitigate the effects of global warming by selectively absorbing $\text{CO}_{2}$ for direct capture. Accurate quantum chemistry simulations are a useful tool to help select and…
The advancement of machine learning technologies has revolutionized the search and optimization of material properties. These algorithms often rely on theoretical calculations, such as density functional theory (DFT), for data inputs and…
The temperature dependence of the Mott metal-insulator transition (MIT) is studied with a VO_2-based two-terminal device. When a constant voltage is applied to the device, an abrupt current jump is observed with temperature. With increasing…
Material scientists are increasingly adopting the use of machine learning (ML) for making potentially important decisions, such as, discovery, development, optimization, synthesis and characterization of materials. However, despite ML's…
High-throughput approximations of quantum mechanics calculations and combinatorial experiments have been traditionally used to reduce the search space of possible molecules, drugs and materials. However, the interplay of structural and…
Ionic mobility determines the rate performance of several applications, such as batteries, fuel cells, and electrochemical sensors and is exponentially dependent on the migration barrier ($E_m$), a difficult to measure/calculate quantity.…
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,…
Iron oxide is a key compound to understand the state of the deep Earth. It has been believed that previously known oxides such as FeO and Fe2O3 will be dominant at the mantle conditions. However, recent observation of FeO2 shed another…
While density functional theory (DFT) serves as a prevalent computational approach in electronic structure calculations, its computational demands and scalability limitations persist. Recently, leveraging neural networks to parameterize the…
The increased availability of computing time, in recent years, allows for systematic high-throughput studies of material classes with the purpose of both screening for materials with remarkable properties and understanding how structural…
Synthesis of advanced inorganic materials with minimum number of trials is of paramount importance towards the acceleration of inorganic materials development. The enormous complexity involved in existing multi-variable synthesis methods…
Accurate evaluation of the thermal conductivity of a material can be a challenging task from both experimental and theoretical points of view. In particular for the nanostructured materials, the experimental measurement of thermal…
In solids, strong repulsion between electrons can inhibit their movement and result in a "Mott" metal-to-insulator transition (MIT), a fundamental phenomenon whose understanding has remained a challenge for over 50 years. A key issue is how…
In the field of interacting electron systems the Mott metal-to-insulator (MI) transition represents one of the pivotal issues. The role played by lattice degrees of freedom for the Mott MI transition and the Mott criticality in a variety of…
Metal to insulator transitions (MITs) driven by strong electronic correlations are common in condensed matter systems, and are associated with some of the most remarkable collective phenomena in solids, including superconductivity and…
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…
The metal-insulator transition (MIT) of the Magneli phase V_4O_7 is studied by means of electronic structure calculations using the augmented spherical wave method. The calculations are based on density functional theory and the local…
We utilize a glass-like structural transition in order to induce a Mott metal-insulator transition in the quasi-two-dimensional organic charge-transfer salt $\kappa$-(BEDT-TTF)$_2$Cu[N(CN)$_2$]Br. In this material, the terminal ethylene…