The lack of mobile holes and relatively high electron mobility in the amorphous phase makes Indium–Gallium–Zinc Oxide (IGZO) stand out among the semiconducting oxides. Investigating atomistic defects,...

While we demonstrate a back-gated (BG) amorphous Indium-Gallium-Zinc-Oxide (a-IGZO) transistors with a nearly ideal subthreshold slope (SS) ~ 60 mV/dec. However, SS degrades when a top-gated (TG) conf...

arXiv abstract link

arXiv abstract link

Two-dimensional materials stabilize perovskites for charge transfer under heat and light irradiation

The development of extreme ultraviolet sources for nanolithography is enabling the production of integrated circuits with feature sizes of less than 10 nm. This necessitates simultaneously the optimiz...

The fabrication of complex nano-scale structures, which is a crucial step in the scaling of (nano)electronic devices, often leads to residual stress in the diff

From the photoelectric effect to the development of contemporary photocatalysts, the hunt for efficient energy conversion has been a tale of invention and discovery. Since 1972, water-splitting photocatalysis has evolved as an effective strategy for advancing toward sustainable energy. The van der Waals (vdW) heterostructures, formed by the vertical stacking of two distinct 2D materials, exhibit unique electronic properties. The weak vdW interactions tend to provide the benefit of efficient carrier separation, thus making them promising candidates for photocatalysis. However, analyzing all possible combinations of 2D materials is impractical through traditional approaches, necessitating the development of predictive models to automate and accelerate the quest. Herein, we propose a hybrid approach using machine learning (ML) in conjugation with first-principles calculations to predict the properties of hexagonal vdW bilayers for application in photocatalysis. Our ML workflow comprises the following major steps: (1) constructing a vast material space of bilayers and their descriptors using a 2D material database, (2) labeling a diverse set of bilayers using density functional theory (DFT) calculations, (3) training the supervised ML models on a labeled data set for binding energy, interlayer distance, band gap, work function, and band edges of heterostructures, (4) evaluating the performance of models on the validation set, and (5) predicting the properties of the unlabeled data set and screening the bilayers feasible for overall water-splitting (OWS) photocatalysis. The computational framework presented here tends to establish the relationship between 2D monolayers and vdW bilayers. Our findings highlight the potential of this approach in accelerating the search for novel photocatalysts by efficiently and accurately predicting their properties, thereby contributing to the broader goal of sustainable energy production.

The generative diffusion method is adapted for use in connection with surface structure determination. The authors introduce here the concepts required to train a diffusion model on structures from sm...

The GW approximation within many-body perturbation theory is the state of the art for computing quasiparticle energies in solids. Typically, Kohn-Sham (KS) eigenvalues and eigenfunctions, obtained fro...

In 1984, imec started as a small research initiative. Today, we proudly stand as the chip lab of the world, driving semiconductor breakthroughs that shape the future.

Integrating In-Ga-Zn-oxide (IGZO) channel transistors in silicon-based ecosystems requires the resilience of the channel material to hydrogen treatment. Standard IGZO, containing 40% In (metal ratio) ...

The success of Kohn–Sham density functional theory in predicting electronic properties from first-principles is key to its ubiquitous presence in condensed matter research. Central to this theory is t...

Corin C. Wagen and Jonathon E. Vandezande (2024) Highlighted by Jan Jensen While this is an interesting paper, a cursory reading (like the o...

arXiv abstract link