[HTML][HTML] MA2Z4 family heterostructures: Promises and prospects
Recent experimental synthesis of ambient-stable MoSi 2 N 4 monolayer has garnered
enormous research interest. The intercalation morphology of MoSi 2 N 4—composed of a …
enormous research interest. The intercalation morphology of MoSi 2 N 4—composed of a …
Research progress on penta-graphene and its related materials: Properties and applications
The most direct and efficient strategy in designing and synthesizing new materials is to
change the structural building units, which could lead to a new paradigm shift. Penta …
change the structural building units, which could lead to a new paradigm shift. Penta …
The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design
Abstract The Joint Automated Repository for Various Integrated Simulations (JARVIS) is an
integrated infrastructure to accelerate materials discovery and design using density …
integrated infrastructure to accelerate materials discovery and design using density …
Unified graph neural network force-field for the periodic table: solid state applications
Classical force fields (FFs) based on machine learning (ML) methods show great potential
for large scale simulations of solids. MLFFs have hitherto largely been designed and fitted …
for large scale simulations of solids. MLFFs have hitherto largely been designed and fitted …
High-throughput screening of nano-hybrid metal–organic-frameworks for photocatalytic CO 2 reduction
M Khwaja, T Harada - Materials Horizons, 2024 - pubs.rsc.org
Photocatalytic conversion of CO2 into fuel feed stocks is a promising method for sustainable
fuel production. A highly attractive class of materials, inorganic-core@ metal–organic …
fuel production. A highly attractive class of materials, inorganic-core@ metal–organic …
When Machine Learning Meets 2D Materials: A Review
The availability of an ever‐expanding portfolio of 2D materials with rich internal degrees of
freedom (spin, excitonic, valley, sublattice, and layer pseudospin) together with the unique …
freedom (spin, excitonic, valley, sublattice, and layer pseudospin) together with the unique …
InterMat: accelerating band offset prediction in semiconductor interfaces with DFT and deep learning
K Choudhary, KF Garrity - Digital Discovery, 2024 - pubs.rsc.org
We introduce a computational framework (InterMat) to predict band offsets of semiconductor
interfaces using density functional theory (DFT) and graph neural networks (GNN). As a first …
interfaces using density functional theory (DFT) and graph neural networks (GNN). As a first …
Predicting van der Waals heterostructures by a combined machine learning and density functional theory approach
Van der Waals (vdW) heterostructures are constructed by different two-dimensional (2D)
monolayers vertically stacked and weakly coupled by van der Waals interactions. VdW …
monolayers vertically stacked and weakly coupled by van der Waals interactions. VdW …
High-Throughput Computational Study and Machine Learning Prediction of Electronic Properties in Transition Metal Dichalcogenide/Two-Dimensional Layered …
Heterostructures formed by transition metal dichalcogenides (TMDs) and two-dimensional
layered halide perovskites (2D-LHPs) have attracted significant attention due to their unique …
layered halide perovskites (2D-LHPs) have attracted significant attention due to their unique …
[HTML][HTML] Database of Wannier tight-binding Hamiltonians using high-throughput density functional theory
KF Garrity, K Choudhary - Scientific data, 2021 - nature.com
Wannier tight-binding Hamiltonians (WTBH) provide a computationally efficient way to
predict electronic properties of materials. In this work, we develop a computational workflow …
predict electronic properties of materials. In this work, we develop a computational workflow …