Related papers: Catalyst design using actively learned machine wit…
The electron density of a molecule or material has recently received major attention as a target quantity of machine-learning models. A natural choice to construct a model that yields transferable and linear-scaling predictions is to…
We characterized CO2 adsorption and diffusion on the missing row reconstructed Cu(100)-O surface using a combination of scanning tunneling microscopy (STM) and density functional theory (DFT) calculations with dispersion. We deposited CO2…
We present a deep learning approach to extract physical parameters (e.g., mobility, Schottky contact barrier height, defect profiles) of two-dimensional (2D) transistors from electrical measurements, enabling automated parameter extraction…
Over the past decade, climate change has become an increasing problem with one of the major contributing factors being carbon dioxide (CO2) emissions; almost 51% of total US carbon emissions are from factories. Current materials used in CO2…
Hemodynamic parameters such as pressure and wall shear stress play an important role in diagnosis, prognosis, and treatment planning in cardiovascular diseases. These parameters can be accurately computed using computational fluid dynamics…
Elucidating the catalytic descriptor that accurately characterizes the structure-activity relationships of typical catalysts for various important heterogeneous catalytic reactions is pivotal for designing high-efficient catalytic systems.…
We propose a dictionary-matching-free pipeline for multi-parametric quantitative MRI image computing. Our approach has two stages based on compressed sensing reconstruction and deep learned quantitative inference. The reconstruction phase…
DPD-Cancer is a graph-attention deep learning framework for predicting small-molecule DPD-Cancer is a graph-attention deep learning framework for predicting small-molecule anti-cancer activity across the NCI-60 panel, trained and evaluated…
Energy loss of energetic ions in solid is crucial in many field, and accurate prediction of the ion stopping power is a long-time goal. Though great efforts have been made, it is still very difficult to find a universal prediction model to…
We present a novel interpretable machine learning model to accurately predict complex rippling deformations of Multi-Walled Carbon Nanotubes(MWCNTs) made of millions of atoms. Atomistic-physics-based models are accurate but computationally…
The adsorption of $CO_2$ on the $Fe_3$$O_4$(001)-($\sqrt{2}$ $\times$ $\sqrt{2}$)R45{\deg} surface was studied experimentally using temperature programmed desorption (TPD), electron spectroscopies (UPS and XPS), and scanning tunneling…
We investigate the graph-based convolutional neural network approach for predicting and ranking gas adsorption properties of crystalline Metal-Organic Framework (MOF) adsorbents for application in post-combustion capture of $\textrm{CO}_2$.…
Adsorption energy is a key descriptor of catalytic reactivity. It is fundamentally defined as the difference between the relaxed total energy of the adsorbate-surface system and that of an appropriate reference state; therefore, the…
Single atom catalysts (SACs) based on 2D materials are identified to be efficient in many catalytic reaction. In this work, the catalytic performance of Pd/Pt embedded planar carbon nitride (CN) for CO oxidation, has been investigated via…
Quantum computing presents a promising alternative to classical computational methods for modeling strongly correlated materials with partially filled d orbitals. In this study, we perform a comprehensive quantum resource estimation using…
Here we show results of first-principles investigations aiming at tuning and controlling the catalytic activity of gold nanoclusters through the design of the underlying support. We show that gold clusters adsorbed on a very thin (2 layers)…
Delafossites are promising candidates for photocatalysis applications because of their chemical stability and absorption in the solar region of the electromagnetic spectrum. For example, CuAlO2 has good chemical stability but has a large…
Large-scale applications are waiting for an optimal CO2 scavenger to reinforce CCS and CCU technologies. We herein introduce and succinctly validate a new philosophy of capturing gaseous CO2 by negatively-charged carbonaceous structures.…
The accurate and precise extraction of information from a modern particle physics detector, such as an electromagnetic calorimeter, may be complicated and challenging. In order to overcome the difficulties we propose processing the detector…
With neural networks growing deeper and feature maps growing larger, limited communication bandwidth with external memory (or DRAM) and power constraints become a bottleneck in implementing network inference on mobile and edge devices. In…