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The design of better exchange-correlation functionals for Density Functional Theory (DFT) is a central challenge of modern electronic structure theory. However, current developments are limited by the mathematical form of the functional,…
Machine-learned interatomic potentials (MLIPs) based on message passing neural networks hold promise to enable large-scale atomistic simulations of complex materials with ab initio accuracy. A number of MLIPs trained on energies and forces…
In a previous paper [Fan Z \textit{et al}. 2021 Phys. Rev. B, \textbf{104}, 104309], we developed the neuroevolution potential (NEP), a framework of training neural network based machine-learning potentials using a natural evolution…
To assess long-term structural integrity under operational conditions in nuclear reactors, SS 316LN with varying nitrogen content is subjected to thermal aging for 20000 hours at 650{\deg}C. While the annealing twins and heterogeneous grain…
The vision of a hydrogen economy demands efficient platforms to close the gap between sustainable proton sources and solid-state hydrogen carriers. Metal hydrides serve as key carriers, yet their synthesis remains constrained by the…
Molybdenum disulfide (MoS2) has emerged as a promising, cost-effective catalyst for hydrogen production via water splitting. We investigate the structural and electrocatalytic properties of MoS2 thin films deposited on different substrates…
Metal-organic frameworks (MOFs) are nanoporous compounds composed of metal ions and organic linkers. MOFs play an important role in industrial applications such as gas separation, gas purification, and electrolytic catalysis. Important MOF…
Developing a universal and precise design framework is crucial to search high-performance catalysts, but it remains a giant challenge due to the diverse structures and sites across various types of catalysts. To address this challenge,…
Accurate and efficient characterization of nanoparticles (NPs), particularly regarding particle size distribution, is essential for advancing our understanding of their structure-property relationships and facilitating their design for…
The combination of density functional theory (DFT) with a multiconfigurational wave function is an efficient way to include dynamical correlation in calculations with multiconfiguration self-consistent field wave functions. These methods…
Honeycomb layered tellurates represent a burgeoning class of multi-functional materials with fascinating crystal-structural versatility and a rich composition space. Despite their multifold capabilities, their compositional diversity…
Unraveling a descriptor that is correlated with catalytic reactivity is essential for fast screening for the optimal catalysts for a given catalytic reaction. Herein, we propose bond-derived orbitalwise coordination number…
Point defect complexes in crystalline silicon composed of hydrogen, nitrogen, and oxygen atoms are studied within density-functional theory (DFT). Ab initio Random Structure Searching (AIRSS) is used to find low-energy defect structures. We…
Heterogeneous catalysts are the most important catalysts in industrial reactions. Nanocatalysts, with size ranging from hundreds of nanometers to the atomic scale, possess activities that are closely connected to their structural…
Deep learning-based drug response prediction (DRP) methods can accelerate the drug discovery process and reduce R\&D costs. Although the mainstream methods achieve high accuracy in predicting response regression values, the regression-aware…
In this study, we address the significant challenge of overcoming limitations in catalytic efficiency for the oxygen evolution reaction (OER). The current linear scaling relationships hinder the optimization of electrocatalytic performance.…
We demonstrate a machine learning approach designed to extract hidden chemistry/physics to facilitate new materials discovery. In particular, we propose a novel method for learning latent knowledge from material structure data in which…
Now-a-days, the development of clean and green energy sources is the prior interest of research due to increasing global energy demand and extensive usage of fossil fuels that create pollutants. Hydrogen has the highest energy density by…
The catalytic properties of Pt2XSe3 (X = Hg, Zn) in hydrogen-electrode- (HER-) based catalysts have been investigated based on state-of-the-art ab initio simulations. Our results show that the late transition metal sites (Hg and Zn) exhibit…
Within first-principles density functional theory (DFT) frameworks, accurate but fast prediction of electronic structures of nanoparticles (NPs) remains challenging. Herein, we propose a machine-learning architecture to rapidly but…