相关论文: Accelerated Discovery of Nitrogen-Coordinated Dual…
Electrode-potential-induced dynamic coordination is an essential factor governing the performance of graphene-embedded single-atom catalysts (SACs). While previous studies have primarily centered on structural dynamics at the metal site,…
Density functional theory (DFT) can be quite advantageous in advancing the field of catalysis because of the microscopic insights it provides, and thus can guide experimental searches of novel catalysts. Several recent works have…
In the present work we investigate the adequacy of broken-symmetry unrestricted density functional theory (DFT) for constructing the potential energy curve of nickel dimer and nickel hydride, as a model for larger bare and hydrogenated…
Photocatalytic hydrogen (H2) production and carbon dioxide (CO2) conversion to methane (CH4) are considered promising solutions for reducing CO2 emissions. However, the development of highly active photocatalysts is essential to efficiently…
Direct air capture (DAC) of carbon dioxide is a critical technology for mitigating climate change, but current materials face limitations in efficiency and scalability. We discover novel DAC materials using a combined machine learning (ML)…
The prospects of observing the non-resonant di-Higgs production in the Standard Model at the proposed high energy upgrade of the LHC, $viz.$ the HE-LHC$~$($\sqrt{s}=27~{\rm TeV}$ and $\mathcal{L} = 15~{\rm ab^{-1}}$) is studied. Various…
For decades of catalysis research, the d-band center theory that correlates the d-band center and the adsorbate binding energy has successfully enabled the accelerated discovery of novel catalyst materials. Recent studies indicate that, on…
Catalyst discovery is paramount to support access to energy and key chemical feedstocks in a post fossil fuel era. Exhaustive computational searches of large material design spaces using ab-initio methods like density functional theory…
High penetration of renewables (RES) and the retirement of thermal units aggravate flexibility scarcity in power systems. Hydrogen-based low-carbon steel production systems possess substantial demand response (DR) potential. This paper…
Developing cost-effective and high-performance electrodes is critical for advancing hydrogen (H$_2$) production through electrochemical water splitting. In this study, we present a novel electrode design by depositing a $\beta$-MnO$_2$…
Great enthusiasm in single atom catalysts (SACs) for the N2 reduction reaction (NRR) has been aroused by the discovery of Metal (M)-Nx as a promising catalytic center. However,the performance of available SACs,including poor activity and…
The hydrogen oxidation reaction (HOR) and hydrogen evolution reaction (HER) play an important role in hydrogen based energy conversion. Recently, the frustrating performance in alkaline media raised debates on the relevant mechanism,…
Hydrogen evolution reaction (HER) has emerged as a promising technique for the production of clean and sustainable energy. In recent years, researchers have been exploring various materials for efficient HER activity. In this study, we…
The Higgs potential is vital to understand the electroweak symmetry breaking mechanism, and probing the Higgs self-interaction is arguably one of the most important physics targets at current and upcoming collider experiments. In…
Graph neural networks (GNNs) have drawn more and more attention from material scientists and demonstrated a high capacity to establish connections between the structure and properties. However, with only unrelaxed structures provided as…
Transition-metal-based layered double hydroxides (TM-LDHs) nanosheets are promising electrocatalysts in the renewable electrochemical energy conversion system, which are regarded as alternatives to noble metal-based materials. In this…
Understanding and prediction of the chemical reactions are fundamental demanding in the study of many complex chemical systems. Reactive molecular dynamics (MD) simulation has been widely used for this purpose as it can offer atomic details…
Efficient and accurate interatomic potential functions are critical to computational study of materials while searching for structures with desired properties. Traditionally, potential functions or energy landscapes are designed by experts…
We investigate the impact of choosing regressors and molecular representations for the construction of fast machine learning (ML) models of thirteen electronic ground-state properties of organic molecules. The performance of each…
Catalyst design is crucial for materials synthesis, especially for complex reaction networks. Strategies like collaborative catalytic systems and multifunctional catalysts are effective but face challenges at the nanoscale. Carbon nanotube…