arXiv subjects

Chen Ming

Publications and source records attributed to Chen Ming.

8 recordsLinked to original sources

arXiv2026

Enhanced Ionic Conductivity of confined Ionic-Liquid in Angstrom-scale 2D channels

Understanding ion-transport under molecular confinement is essential for developing next-generation energy technologies, where ionic motion often occurs within nanoscale or angstrom-scale channels. In this study, we use the model system of 1-ethyl-3-methylimidazolium bis(trifluoromethanesulfonyl)imide ([EMIM]+[TFSI]-) confined within angstrom-scale slit-shaped 2D channels fabricated via van der Waals assembly to exemplify a broader class of confined ionic liquids.This system provides a well-defined platform to unravel generic features of ion transport under extreme confinement. By systematically varying the channel height h, we demonstrate a non-monotonic conductivity dependence on confinement, with a maximum 26.7 S/m at confining height, 1.02 nm, over 30 times of the bulk value for these ionic liquids. The variation of conductivity with confinement arises from structural rearrangements of ionic layers in the slit channel. Enhanced values of conductivity occur under confinements that promote the breakup of ion pairs and larger clusters, thereby increasing the number of free ions. Stronger confinement (h, 0.68 nm) also leads to steric hindrance, lowering conductivity below bulk values. Furthermore, introducing co-solvents with a higher dielectric constant and lower viscosity, such as acetonitrile (ACN), amplifies conductivity to ~145 S/m. Comparative studies using ACN, dimethyl carbonate and diethyl carbonate highlight that both large dielectric constant and low viscosity critically govern ion transport under confinement, as also supported by molecular dynamics simulations. Overall, this work establishes confined [EMIM]+[TFSI]- as a representative system for probing mechanisms of nano- and angstrom-scale ion transport, demonstrating how nanoconfinement and the solvent environment can be systematically tuned to manipulate ionic conductivity at the molecular level.

physics.chem-ph↗

arXiv2023

GPT-assisted learning of structure-property relationships by graph neural networks: Application to rare-earth doped phosphors

Applications of machine learning techniques in materials science are often based on two key ingredients, a set of empirical descriptors and a database of a particular material property of interest. The advent of graph neural networks, such as the Crystal Graph Convolutional Neural Network (CGCNN), demonstrates the possibility of directly mapping the relationship between material structures and properties without employing empirical descriptors. Another exciting recent advancement is in large language models such as OpenAI's GPT-4, which demonstrates competency at reading comprehension tasks and holds great promise for accelerating the acquisition of databases on material properties. Here, we utilize the combination of GPT-4 and CGCNN to develop rare-earth doped phosphors for solid-state lighting. GPT-4 is applied to data-mine chemical formulas and emission wavelengths of 264 Eu(II)-doped phosphors from 274 papers. A CGCNN model is trained on the acquired dataset, achieving a test $R^2$ of 0.77. The model is then used to screen over 40,000 inorganic materials to make predictions on the emission wavelengths. We also demonstrate the possibility of leveraging transfer learning to fine-tune a bandgap-predicting CGCNN model towards the prediction of phosphor emission wavelengths. The workflow requires minimal human supervision, little domain knowledge about phosphors, and is generalizable to other material properties.

cond-mat.mtrl-sci↗

arXiv2022

Target Geometry Estimation Using Deep Neural Networks in Sonar Sensing

Accurate imaging of target shape is a crucial aspect of wideband FM biosonar in echolocating bats, for which we have developed new algorithms that provide a solution for the shape of complicated targets in the computational domain. We use recurrent neural networks and convolutional neural networks to determine the number of glints (i.e., major reflecting surfaces) making up the target's structure and the distances between the glints (target shape in sonar). Echoes are dechirped relative to broadcasts, and the dechirped spectrograms are scanned in short time segments to find local spectral ripple patterns arising from different interglint delay separations. By proceeding in successive time-window slices, we mimic time-frequency neural processing in the bat's auditory system as a novel means of real-time target discrimination for sonar sensing in robotics.

cs.SD↗

arXiv2015

Pricing-based Distributed Energy-Efficient Beamforming for MISO Interference Channels

In this paper, we consider the problem of maximizing the weighted sum energy efficiency (WS-EE) for multi-input single-output (MISO) interference channels (ICs) which is well acknowledged as general models of heterogeneous networks (HetNets), multicell networks, etc. To address this problem, we develop an efficient distributed beamforming algorithm based on a pricing mechanism. Specifically, we carefully introduce a price metric for distributed beamforming design which fortunately allows efficient closed-form solutions to the per-user beam-vector optimization problem. The convergence of the distributed pricing-based beamforming design is theoretically proven. Furthermore, we present an implementation strategy of the proposed distributed algorithm with limited information exchange. Numerical results show that our algorithm converges much faster than existing algorithms, while yielding comparable, sometimes even better performance in terms of the WS-EE. Finally, by taking the backhaul power consumption into account, it is interesting to show that the proposed algorithm with limited information exchange achieves better WS-EE than the full information exchange based algorithm in some special cases.

cs.IT↗

arXiv2013

Predicting the growth rate of helium bubbles in metal tritide

Helium bubbles nucleation and growth in metals or metal tritide is a long-standing problem attracting considerable attention in nuclear industry but the mechanism remains indistinct and predicting the growth rate of helium bubble is inexistence still up to new. Here, the rate of helium bubbles nucleation and growth in metal tritide is developed based on a dynamical model, which describes the diameter of helium bubbles increasing linearly as t**(1/3) in titanium tritide at room temperature, agreeing quite well with the experimental phenomenon. The way of reducing storage temperature from 300 to 225 K or increasing the helium atoms diffusion barrier from 0.81 to 1.1 eV can effectively restrain bubbles growth and prolong lifetime of titanium tritide more than 4 times, which provides a useful reference to relevant experiment exploration and applications. This model also can be used to predict lifetime of new tritium-storage materials and plasma facing materials in nuclear industry.

cond-mat.mtrl-sci↗

arXiv2013

If graphynes turn into graphene: the thermal stability study

The thermal stability of $α$-, $β$-, 6,6,12-graphyne and graphdiyne was studied by a statistic model, which was seriously tested by classical molecular dynamics simulations. By first-principles calculations of related potential energy curves, the model predicts that all the lifetime of free-standing single layer graphynes considered is more than 10$^{44}$ years at room temperature. When the temperature gets up to 1000 K, they are still very stable, but quickly turn into graphene if the temperature is about 2000 K

cond-mat.mes-hall↗

arXiv2013

Tuning the Conductance of Monatomic Carbon Chain

Ab initio calculations show that the conductance of short monatomic carbon chain can be dramatically modified by adhering a single H, N, or O atom to the chain. For example, the conductance of the pristine chain gets about two orders of magnitude smaller if an H atom is adhered to the chain. By a statistical model, the structure of the carbon chain with the single atom adhered is found to be quite stable at room temperature, indicating that the method can be used to tune the conductance of monatomic carbon chain.

cond-mat.mes-hall↗

arXiv2013

A scheme for realizing continuously tunable spectrum in the visible light region based on monatomic carbon chains

We propose a scheme for realizing the continuously tunable spectrum based on monatomic carbon chains. By hybrid density functional calculations, we first show that the direct band gap of monatomic carbon chains change continuously from 1.58 to 3.8 eV as strain is applied from -5 to 10% to the chain, with separated Van Hove singularity peaks enhanced. To realize this tunability, a realistic stretching device is proposed by contacting the chain with graphene sheets, which can apply up to 9% elongation to the chain, yielding tunable light-emitting wavelengths from 345 to 561 nm.

cond-mat.mes-hall↗