Related papers: Accelerating Discovery of Metal-Insulator Transiti…

Superconductivity has been the focus of enormous research effort since its discovery more than a century ago. Yet, some features of this unique phenomenon remain poorly understood; prime among these is the connection between…

We propose an approach to materials prediction that uses a machine-learning interatomic potential to approximate quantum-mechanical energies and an active learning algorithm for the automatic selection of an optimal training dataset. Our…

Traditional materials discovery approaches - relying primarily on laborious experiments - have controlled the pace of technology. Instead, computational approaches offer an accelerated path: high-throughput exploration and characterization…

We study a bandwidth controlled Mott metal-insulator transition (MIT) between a Fermi liquid metal and a quantum spin-liquid insulator at half-filling in three dimensions (3D). Using a slave rotor approach, and incorporating gauge field…

Understanding how structural flexibility affects the properties of metal-organic frameworks (MOFs) is crucial for the design of better MOFs for targeted applications. Flexible MOFs can be studied with molecular dynamics simulations, whose…

The metal-insulator transition (MIT) is one of the most dramatic manifestations of electron correlations in materials. Various mechanisms producing MITs have been extensively considered, including the Mott (electron localization via Coulomb…

Metal-to-insulator transitions (MIT) can be driven by a number of different mechanisms, each resulting in a different type of insulator -- Change in chemical potential can induce a transition from a metal to a band insulator; strong…

We perform machine learning (ML) simulations and density functional theory (DFT) calculations to search for materials with low lattice thermal conductivity, $\kappa_L$. Several cadmium (Cd) compounds containing elements from the…

The electrical control of a material's conductivity is at the heart of modern electronics. Conventionally, this control is achieved by tuning the density of mobile charge carriers. A completely different approach is possible in Mott…

Discovering materials that must simultaneously satisfy multiple competing constraints remains a central challenge in computational materials design, particularly in data-scarce regimes where conventional data-driven approaches are least…

Metal-organic frameworks (MOFs) have been widely investigated for challenging catalytic transformations due to their well-defined structures and high degree of synthetic tunability. These features, at least in principle, make MOFs ideally…

The magnetic field-induced superconductor-insulator-metal transition (SIMT) in partially deuterated $\kappa$-(BEDT-TTF)$_2$Cu[N(CN)$_2$]Br, which is just on the Mott boundary, has been observed using the infrared magneto-optical imaging…

Accurately predicting when and how materials fail is critical to designing safe, reliable structures, mechanical systems, and engineered components that operate under stress. Yet, fracture behavior remains difficult to model across the…

The thermoelectric performance of materials exhibits complex nonlinear dependencies on both elemental types and their proportions, rendering traditional trial-and-error approaches inefficient and time-consuming for material discovery. In…

Thermoelectric materials, which can convert waste heat into electricity or act as solid-state Peltier coolers, are emerging as key technologies to address global energy shortages and environmental sustainability. However, discovering…

Metal-insulator transitions (MIT) occurring in non-adiabatic conditions can evolve through high-energy intermediate states that are difficult to observe and control via static methods. By monitoring the out-of-equilibrium structural…

$V_2O_3$ has long been studied as a prototypical strongly correlated material. The difficulty in obtaining clean, well ordered surfaces, however, hindered the use of surface sensitive techniques to study its electronic structure. Here we…

We develop new transfer learning algorithms to accelerate prediction of material properties from ab initio simulations based on density functional theory (DFT). Transfer learning has been successfully utilized for data-efficient modeling in…

We present the Materials Learning Algorithms (MALA) package, a scalable machine learning framework designed to accelerate density functional theory (DFT) calculations suitable for large-scale atomistic simulations. Using local descriptors…

Transition metal oxides such as vanadium dioxide (VO$_2$), niobium dioxide (NbO$_2$), and titanium sesquioxide (Ti$_2$O$_3$) are known to undergo a temperature-dependent metal-insulator transition (MIT) in conjunction with a structural…