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Machine learning and deep learning have gained popularity and achieved immense success in Drug discovery in recent decades. Historically, machine learning and deep learning models were trained on either structural data or chemical…
Nowadays, electrochemical reduction of CO$_2$ has been considered as an effective method to solve the problem of global warming. The primary challenge in studying the mechanism is to determine the adsorption states of CO$_2$, since…
Nanoalloys offer a unique opportunity to tailor chemical properties through changes in composition, shape, and size. However, this flexibility introduces complexity that challenges both experimental and conventional theoretical methods. In…
This study introduces BIRDSHOT, an integrated Bayesian materials discovery framework designed to efficiently explore complex compositional spaces while optimizing multiple material properties. We applied this framework to the CoCrFeNiVAl…
Existing molecular machine learning force fields (MLFFs) generally focus on the learning of atoms, molecules, and simple quantum chemical properties (such as energy and force), but ignore the importance of electron density (ED) $\rho(r)$ in…
Refractory complex concentrated alloys (RCCAs) are of significant interest for advanced high-temperature applications, owing to their broad compositional range and potential for attractive mechanical properties and oxidation resistance.…
Directly generating material structures with optimal properties is a long-standing goal in material design. One of the fundamental challenges lies in how to overcome the limitation of traditional generative models to efficiently explore the…
The practically unlimited high-dimensional composition space of high-entropy materials (HEMs) has emerged as an exciting platform for functional materials design and discovery. However, the identification of stable and synthesizable HEMs…
High electric fields can significantly alter catalytic environments and the resultant chemical processes. Such fields arise naturally in biological systems but can also be artificially induced through localized excitations at nanoscale.…
Direct access to transition state energies at low computational cost unlocks the possibility of accelerating catalyst discovery. We show that the top performing graph neural network potential trained on the OC20 dataset, a related but…
A Density Functional study predicting a heterogeneous-catalyzed reaction giving rise to biodiesel, was performed. Triacetin was used as a model of triglyceride in the presence of an Au(111) surface as the heterogeneous catalyst substrate.…
In this paper, we introduce a deep learning solution for video activity recognition that leverages an innovative combination of convolutional layers with a linear-complexity attention mechanism. Moreover, we introduce a novel quantization…
Electrochemical reduction is a promising way to make use of CO$_2$ as feedstock for generating renewable fuel and valuable chemicals. Several metals can be used in the electrocatalyst to generate CO and formic acid but hydrogen formation is…
This study investigates the structural, electronic, and catalytic properties of pristine and doped C$_4$N$_2$ nanosheets as potential electrocatalysts for the hydrogen evolution reaction. The pristine C$_{36}$N$_{18}$ nanosheets exhibit…
Heterogeneous catalysts possess complex surface and bulk structures, relatively poor intrinsic contrast, and often a sparse distribution of the catalytic nanoparticles (NPs), posing a significant challenge for image segmentation, including…
Unique catalytic potential of metal surfaces has encouraged a great number of basic and applied studies. The manuscript highlights the general regularities in a field on the grounds of strong interrelation between catalytic, kinetic and…
Direct conversion of carbon dioxide (CO2) to high-energy fuels and high-value chemicals is a fascinating sustainable strategy. For most of the current electrocatalysts for CO2 reduction, however, multi-carbon products are inhibited by large…
Recent promising results in auditory attention decoding (AAD) using scalp electroencephalography (EEG) have motivated the exploration of cEEGrid, a flexible and portable ear-EEG system. While prior cEEGrid-based studies have confirmed the…
The effect of elastic strains on the catalytic activity for the hydrogen evolution reaction (HER) and the oxygen reduction reaction (ORR) was analyzed on thirteen late transition metals: eight (111) surfaces of fcc metals (Ni, Cu, Pd, Ag,…
Reactive chemistry of molecular hydrogen at surfaces, notably dissociative sticking and hydrogen evolution, plays a crucial role in energy storage and fuel cells. Theoretical studies can help to decipher underlying mechanisms and reaction…