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Motivated by the recent realization of graphene sensors to detect individual gas molecules, we investigate the adsorption of H2O, NH3, CO, NO2, and NO on a graphene substrate using first-principles calculations. The optimal adsorption…
This study focuses on membrane-based systems for CO$_2$ separation, addressing the urgent need for efficient carbon capture solutions to mitigate climate change. Linear regression models, based on membrane equations, were utilized to…
Metal-organic frameworks (MOFs) are excellent candidates for water harvesting due to their tunable pore environments, which can be precisely engineered to capture and release water in arid conditions. Integrating artificial intelligence…
Grasp planning and estimation have been a longstanding research problem in robotics, with two main approaches to find graspable poses on the objects: 1) geometric approach, which relies on 3D models of objects and the gripper to estimate…
In the molecular beam epitaxy of oxide films, the cation (Sn, Ga) or dopant (Sn) incorporation does not follow the vapor pressure of the elemental metal sources, but is enhanced by several orders of magnitude for low source temperatures.…
The increasing demand for sustainable energy solutions necessitates advancements in hydrogen storage technologies. This study investigates the hydrogen adsorption characteristics of graphene and a (8,0) carbon nanotube (CNT) decorated with…
We compute ionic free energy adsorption profiles at aqueous graphene interface by developing a self-consistent approach. To do so, we design a microscopic model for water and put the liquid on an equal footing with the graphene described by…
Identification of unknown physical processes and parameters of groundwater contaminant sources is a challenging task due to their ill-posed and non-unique nature. Numerous works have focused on determining nonlinear physical processes…
The study of the interaction between graphene oxide and arsenic is of great relevance towards the development of adsorbent materials and as a way to understand how these two materials interact in the environment. In this work we show that…
In this study, we focus on the graph representation learning (a.k.a. network embedding) in attributed graphs. Different from existing embedding methods that treat the incorporation of graph structure and semantic as the simple combination…
Batteries are critical for electrified transportation and aviation, yet thermodynamic understanding of electrode materials remains lacking, as indicated by the often-seen violation of the second law of thermodynamics of open-circuit voltage…
In nearly compensated graphene, disorder-assisted electron-phonon scattering or "supercollisions" are responsible for both quasiparticle recombination and energy relaxation. Within the hydrodynamic approach, these processes contribute weak…
Water dissociation is of fundamental importance in scientific fields and has drawn considerable interest in diverse technological applications. However, the high activation barrier of breaking the O-H bond within the water molecule has been…
For a gas-solid interfacial system where chemical species undergo reversible adsorption, we develop a mesoscopic stochastic modeling method that simulates both gas-phase hydrodynamics and surface coverage dynamics by coupling the Langmuir…
A universal interatomic potential for an arbitrary set of chemical elements is urgently needed in computational materials science. Graph convolution neural network (GCN) has rich expressive power, but previously was mainly employed to…
The design and production of novel 2-dimensional materials has seen great progress in the last decade, prompting further exploration of the chemistry of such materials. Doping and hydrogenating graphene is an experimentally realised method…
Precise characterization of the graphene/water interface has been hindered by experimental inconsistencies and limited molecular-level access to interfacial structures. In this work, we present a novel integrated computational approach that…
Deep Graph Neural Networks struggle with oversmoothing. This paper introduces a novel, physics-inspired GNN model designed to mitigate this issue. Our approach integrates with existing GNN architectures, introducing an entropy-aware message…
We provide novel random surface density functional theory (RSDFT) formulation in the case of geometric heterogeneous surface of solid media which is essential for description of thermodynamic properties of confined fluids. The major…
A first-principles based methodology for efficiently and accurately finding thermodynamically stable and metastable atomic structures is introduced and benchmarked. The approach is demonstrated for gas-phase metal-oxide clusters in…