Related papers: Seeing moir\'e: convolutional network learning app…
Moir\'e heterostructures provide a powerful framework for tailoring electronic band structures via controlled long-range periodic superlattice potentials. Beyond widely studied moir\'e-tailored flat bands, folded band structures can host…
Magnetic ordering in two-dimensional (2D) materials has recently emerged as a promising platform for data storage, computing, and sensing. To advance these developments, it is vital to gain a detailed understanding of how the magnetic order…
Moir\'e structures in small-angle-twisted bilayers of two-dimensional semiconductors with a broken-symmetry interface form arrays of ferroelectric domains with periodically alternating out-of-plane polarization. Here, we propose a network…
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…
Moir\'e superlattices created by the twisted stacking of two-dimensional crystalline monolayers can host electronic bands with flat energy dispersion in which interaction among electrons is strongly enhanced. These superlattices can also…
Microstructure--property relationships are key to effective design of structural materials for advanced applications. Advances in computational methods enabled modeling microstructure-sensitive properties using 3D models (e.g., finite…
Moir\'e heterostructures consisting of transition metal dichalcogenide (TMD) hetero- and homobilayers have emerged as a promising material platform to study correlated electronic states. Optical signatures of strong correlations in the form…
Moir\'e patterns arise from spectral aliasing between display pixel lattices and camera sensor grids, manifesting as anisotropic, multi-scale artifacts that pose significant challenges for digital image demoir\'eing. We propose Moir\'eNet,…
The moir\'e superlattices formed by stacking 2D semiconducting transition metal dichalcogenides (TMDs) with twisting angle or lattice mismatch have provided a versatile platform with unprecedented tunability for exploring many frontier…
We introduce a generalizable, physics informed strategy for generating training data that enables a machine learning force field accurate over a broad range of twist angles and stacking layer numbers in moire systems. Applying this to…
Recent proposals for the realization of time-reversal symmetry breaking and topological superconductivity in twisted nodal superconductors have led to a surge of theoretical and experimental studies of these systems, marking one of the…
Modern scanning probe techniques, like scanning tunneling microscopy (STM), provide access to a large amount of data encoding the underlying physics of quantum matter. In this work, we analyze how convolutional neural networks (CNN) can be…
The screening of novel materials is an important topic in the field of materials science. Although traditional computational modeling, especially first-principles approaches, is a very useful and accurate tool to predict the properties of…
Two-dimensional (2D) materials have received considerable attention as possible electrodes in Li-ion batteries (LIBs), although a deeper understanding of the Li adsorption behavior as well as broad screening of the materials space is still…
We derive electronic tight-binding Hamiltonians for strained graphene, hexagonal boron nitride and transition metal dichalcogenides based on Wannier transformation of {\it ab initio} density functional theory calculations. Our microscopic…
In bilayers of semiconducting transition metal dichalcogenides, the twist angle between layers can be used to introduce a highly regular periodic potential modulation on a length scale that is large compared to the unit cell. In such…
Piezoelectric materials are widely used in all kinds of industries such as electric cigarette lighters, diesel engines and x-ray shutters. However, discovering high-performance and environmentally friendly (e.g. lead-free) piezoelectric…
Machine learning is rapidly accelerating materials and chemical discovery, but most current models target energies, forces, or selected molecular properties rather than the underlying many-body electronic structure. Learning…
Moir\'e patterns are a central motif in van der Waals heterostructures arising from the superposition of two-dimensional (2D) incommensurate lattices. These patterns reveal a wealth of correlated effects, influencing electronic, magnetic,…
Twisted bilayers of two-dimensional semiconductors offer a versatile platform to engineer quantum states for charge carriers using moir\'e superlattice effects. Among the systems of recent interest are twistronic MoSe${}_{2}$/WSe${}_{2}$…