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Superconducting materials are of significant technological relevance for a broad range of applications, and intense research efforts aim at enhancing the critical temperature $T_{c}$. Intriguingly, while numerous studies have explored…
Designing terahertz sensors with high sensitivity to detect nanoscale thin films and single biomolecule presents a significant challenge, and addressing these obstacles is crucial for unlocking their full potential in scientific research…
The methods of density-functional perturbation theory may be used to calculate various physical response properties of insulating crystals including elastic, dielectric, Born charge, and piezoelectric tensors. These and other important…
We have investigated the electronic, vibrational, optical, thermal and piezoelectric properties of LiNbO$_3$, LiTaO$_3$ and Li$_2$NbTaO$_6$ using the first-principles calculation based on the density functional theory. It also shows…
Machine learning techniques are utilized to estimate the electronic band gap energy and forecast the band gap category of materials based on experimentally quantifiable properties. The determination of band gap energy is critical for…
Historically, materials discovery has been driven by a laborious trial-and-error process. The growth of materials databases and emerging informatics approaches finally offer the opportunity to transform this practice into data- and…
Designing topological materials with specific topological indices is a complex inverse problem, traditionally tackled through manual, intuition-driven methods that are neither scalable nor efficient for exploring the vast space of possible…
We discover many new crystalline solid materials with fast single crystal Li ion conductivity at room temperature, discovered through density functional theory simulations guided by machine learning-based methods. The discovery of new solid…
Despite over a decade of intense research efforts, the full potential of two-dimensional transition metal dichalcogenides continues to be limited by major challenges. The lack of compatible and scalable dielectric materials and integration…
van der Waals stacking of two-dimensional (2D) materials offers a powerful platform for engineering material interfaces with tailored electronic and optical properties. While most van der Waals multilayers have featured inorganic…
We report a high throughput computational search for two-dimensional ferroelectric materials. The starting point is 252 pyroelectric materials from the computational 2D materials database (C2DB) and from these we identify 64 ferroelectric…
Artificial Intelligence (AI) in materials science is driving significant advancements in the discovery of advanced materials for energy applications. The recent GNoME protocol identifies over 380,000 novel stable crystals. From this, we…
The effective mass of charge carriers is a fundamental descriptor of the electronic structure of materials, and can be used to assess performance in electronics applications, or to screen for thermoelectrics and transparent conductors.…
We present the Electronic Tensor Reconstruction Algorithm (ELECTRA) - an equivariant model for predicting electronic charge densities using floating orbitals. Floating orbitals are a long-standing concept in the quantum chemistry community…
The combinations of machine learning with ab initio methods have attracted much attention for their potential to resolve the accuracy-efficiency dilemma and facilitate calculations for large-scale systems. Recently, equivariant message…
The dielectric constant of CaCu3Ti4O12 is measured with a different electrode geometry than previous measurements. Similar colossal values are obtained showing that they are "intrinsic" to the material. We show that in braced structures…
One of the main goals and challenges of materials discovery is to find the best candidates for each interest property or application. Machine learning rises in this context to efficiently optimize this search, exploring the immense…
Lattice thermal conductivity (LTC) is a critical parameter for thermal transport properties, playing a pivotal role in advancing thermoelectric materials and thermal management technologies. Traditional computational methods, such as…
We introduce machine learning (ML) models that predict the electronic structure of materials across a wide temperature range. Our models employ neural networks and are trained on density functional theory (DFT) data. Unlike most other ML…
Machine-learning interatomic potentials (MLIPs) have enabled molecular dynamics at near ab initio accuracy, yet remain limited to energies and forces by construction, leaving electronic observables such as dipole moments and…