Related papers: Accelerating Discovery of Metal-Insulator Transiti…

The first-order metal-insulator transition (MIT) in paramagnetic $V_{2}O_{3}$ is studied within the ab-initio scheme LDA+DMFT, which merges the local density approximation (LDA) with dynamical mean field theory (DMFT). With a fixed value of…

We report the observation of a parallel magnetic field induced metal-insulator transition (MIT) in a high-mobility two-dimensional electron gas (2DEG) for which spin and localization physics most likely play no major role. The high-mobility…

Dynamical mean-field theory (DMFT) has been employed in conjunction with density functional theory (DFT+DMFT) to investigate the metal-insulator transition (MIT) of strongly correlated $3d$ electrons due to quantum confinement. We shed new…

Even though thermodynamic energy-based crystal structure prediction (CSP) has revolutionized materials discovery, the energy-driven CSP approaches often struggle to identify experimentally realizable metastable materials synthesized through…

Accurately predicting magnetic behavior across diverse materials systems remains a longstanding challenge due to the complex interplay of structural and electronic factors and is pivotal for the accelerated discovery and design of…

We present a machine-learning guided approach to predict saturation magnetization (MS) and coercivity (HC) in Fe-rich soft magnetic alloys, particularly Fe-Si-B systems. ML models trained on experimental data reveals that increasing Si and…

The recent explosion of machine learning (ML) and artificial intelligence (AI) shows great potential in the breakthrough of metal additive manufacturing (AM) process modeling. However, the success of conventional machine learning tools in…

Thermoelectric materials can achieve direct energy conversion between electricity and heat, thus can be applied to waste heat harvesting and solid-state cooling. The discovery of new thermoelectric materials is mainly based on experiments…

This review first describes the evidence that strongly suggests the existence of the metal-insulator transition (MIT) in a two-dimensional electron system in Si regardless of the amount of disorder. Extensive studies of the charge dynamics…

Metal-Organic Frameworks (MOFs) are materials with a high degree of porosity that can be used for applications in energy storage, water desalination, gas storage, and gas separation. However, the chemical space of MOFs is close to an…

The underlying mechanism of the metal-to-insulator transition (MIT) in BaVS$_3$ is investigated, using dynamical mean-field theory in combination with density functional theory. It is shown that correlation effects are responsible for a…

High-throughput computational screening has emerged as a critical component of materials discovery. Direct density functional theory (DFT) simulation of inorganic materials and molecular transition metal complexes is often used to describe…

Quantitative descriptions of the structure-thermal property correlation have been a bottleneck in designing materials with superb thermal properties. In the past decade, the first-principles phonon calculations using density functional…

Despite decades of experimental and theoretical efforts, the origin of metal-insulator transitions (MIT) in strongly-correlated materials is one of the main longstanding problems in condensed matter physics. An archetypal example is V2O3,…

This work examines challenges associated with the accuracy of machine-learned force fields (MLFFs) for bulk solid and liquid phases of d-block elements. In exhaustive detail, we contrast the performance of force, energy, and stress…

Metal-to-insulator transitions (MITs) are a dramatic manifestation of strong electron correlations in solids1. The insulating phase can often be suppressed by quantum tuning, i.e. varying a nonthermal parameter such as chemical composi-…

Metal-insulator transitions driven by magnetic fields have been extensively studied in 2D, but a 3D theory is still lacking. Motivated by recent experiments, we develop a scaling theory for the metal-insulator transitions in the…

First-order phase transitions in solids are notoriously challenging to study. The combination of change in unit cell shape, long range of elastic distortion, and flow of latent heat leads to large energy barriers resulting in domain…

The emergent behavior of quantum materials is governed by their electronic structure, which can be experimentally probed by photoemission spectroscopy techniques that generate a four-dimensional dataset of energy and momentum. However, 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…