106 research outputs found
Deep Reinforcement Learning Optimizes Graphene Nanopores for Efficient Desalination
Two-dimensional nanomaterials, such as graphene, have been extensively
studied because of their outstanding physical properties. Structure and
geometry optimization of nanopores on such materials is beneficial for their
performances in real-world engineering applications, like water desalination.
However, the optimization process often involves very large number of
experiments or simulations which are expensive and time-consuming. In this
work, we propose a graphene nanopore optimization framework via the combination
of deep reinforcement learning (DRL) and convolutional neural network (CNN) for
efficient water desalination. The DRL agent controls the growth of nanopore by
determining the atom to be removed at each timestep, while the CNN predicts the
performance of nanoporus graphene for water desalination: the water flux and
ion rejection at a certain external pressure. With the synchronous feedback
from CNN-accelerated desalination performance prediction, our DRL agent can
optimize the nanoporous graphene efficiently in an online manner. Molecular
dynamics (MD) simulations on promising DRL-designed graphene nanopores show
that they have higher water flux while maintaining rival ion rejection rate
compared to the normal circular nanopores. Semi-oval shape with rough edges
geometry of DRL-designed pores is found to be the key factor for their high
water desalination performance. Ultimately, this study shows that DRL can be a
powerful tool for material design.Comment: Yuyang Wang and Zhonglin Cao contributed equally to this wor
Neural Network Predicts Ion Concentration Profiles under Nanoconfinement
Modeling the ion concentration profile in nanochannel plays an important role
in understanding the electrical double layer and electroosmotic flow. Due to
the non-negligible surface interaction and the effect of discrete solvent
molecules, molecular dynamics (MD) simulation is often used as an essential
tool to study the behavior of ions under nanoconfinement. Despite the accuracy
of MD simulation in modeling nanoconfinement systems, it is computationally
expensive. In this work, we propose neural network to predict ion concentration
profiles in nanochannels with different configurations, including channel
widths, ion molarity, and ion types. By modeling the ion concentration profile
as a probability distribution, our neural network can serve as a much faster
surrogate model for MD simulation with high accuracy. We further demonstrate
the superior prediction accuracy of neural network over XGBoost. Lastly, we
demonstrated that neural network is flexible in predicting ion concentration
profiles with different bin sizes. Overall, our deep learning model is a fast,
flexible, and accurate surrogate model to predict ion concentration profiles in
nanoconfinement
Two Heads Are Better Than One: Improving Fake News Video Detection by Correlating with Neighbors
The prevalence of short video platforms has spawned a lot of fake news
videos, which have stronger propagation ability than textual fake news. Thus,
automatically detecting fake news videos has been an important countermeasure
in practice. Previous works commonly verify each news video individually with
multimodal information. Nevertheless, news videos from different perspectives
regarding the same event are commonly posted together, which contain
complementary or contradictory information and thus can be used to evaluate
each other mutually. To this end, we introduce a new and practical paradigm,
i.e., cross-sample fake news video detection, and propose a novel framework,
Neighbor-Enhanced fakE news video Detection (NEED), which integrates the
neighborhood relationship of new videos belonging to the same event. NEED can
be readily combined with existing single-sample detectors and further enhance
their performances with the proposed graph aggregation (GA) and debunking
rectification (DR) modules. Specifically, given the feature representations
obtained from single-sample detectors, GA aggregates the neighborhood
information with the dynamic graph to enrich the features of independent
samples. After that, DR explicitly leverages the relationship between debunking
videos and fake news videos to refute the candidate videos via textual and
visual consistency. Extensive experiments on the public benchmark demonstrate
that NEED greatly improves the performance of both single-modal (up to 8.34% in
accuracy) and multimodal (up to 4.97% in accuracy) base detectors. Codes are
available in https://github.com/ICTMCG/NEED.Comment: To appear in ACL 2023 Finding
Experimental Study on Uniaxial Compression of Bamboo Poles with Different Reinforcements
The natural round bamboo is a kind of ecological building material with many excellent physical and mechanical characteristics, such as fast growth, high strength and good environmental performance. However, the natural round bamboos were barely used for its worse durability and easiness to crack compared other bamboo productions after secondary operation. In order to improve the safety and durability of the round bamboo structure, the axial compression test of the GFRP (glass fiber-reinforced polymer) and/or mortar reinforcing cracked bamboo was conducted. The 20 cm tall round bamboo column specimens were divided into five categories: the first without cracks and reinforcement, the second with cracks but without reinforcement, the third with cracks and full GFRP reinforcement, the forth with cracks and fulfil of cement motrar, and the last with cracks and reinforced using both GFRP and cement mortar. The bearing capacity and the failure modes were observed and studied. It was found that the composite reinforcement of GFRP and mortar could significantly increase the bearing capacity of the cracked round bamboos, and avoid brittle failure through improving the ductility of the specimens
Experimental Study on Uniaxial Compression of Bamboo Nodes Using 3D Scanning Technique
Bamboo is a kind of ecological building material for its physical and mechanical characteristics, such as fast growth, high yield, high strength, high toughness and good environmental performance. However, there are few studies on the influence of bamboo node structure about the mechanical properties of bamboo, and it is difficult to accurately determine the cross-section area of the bamboo node. In this paper, the three-dimensional scanner was combined with the reverse modeling technology to accurately obtain the cross-sectional area of the bamboo node. The bamboo node was subjected to axial compression test. Based on the experimental results, it was confirmed that the compressive strength of the bamboo node increased from the bottom to the top. The experimental results also showed that the difference in the degree of cracks has an effect on the bamboo break mode. Bamboo nodes with severe cracks and uneven distribution on the surface had the largest degree of expansion at the original deep cracks or the original surface through cracks. Bamboo nodes with slight cracks and even distribution or without cracks on the surface were uniformly expanding at the lower part when they were broken
Effect of physical and chemical pressure on the superconductivity of caged-type quasiskutterudite Lu5Rh6Sn18
Lu5Rh6Sn18 is one of the caged-type quasiskutterudite superconductors with
superconducting transition temperature Tc = 4.12 K. Here, we investigate the
effect of pressure on the superconductivity in Lu5Rh6Sn18 by combining high
pressure electrical transport, synchrotron x-ray diffraction (XRD) and chemical
doping. Application of high pressure can enhance both the metallicity and the
superconducting transition temperature in Lu5Rh6Sn18. Tc is found to show a
continuous increase reaching up to 5.50 K at 11.4 GPa. Our high pressure
synchrotron XRD measurements demonstrate the stability of the pristine crystal
structure up to 12.0 GPa. In contrast, Tc is suppressed after the substitution
of La ions in Lu sites, inducing negative chemical pressure. Our study provides
valuable insights into the improvement of superconductivity in caged compounds.Comment: 9 pages, 8 figure
An Autonomous Large Language Model Agent for Chemical Literature Data Mining
Chemical synthesis, which is crucial for advancing material synthesis and
drug discovery, impacts various sectors including environmental science and
healthcare. The rise of technology in chemistry has generated extensive
chemical data, challenging researchers to discern patterns and refine synthesis
processes. Artificial intelligence (AI) helps by analyzing data to optimize
synthesis and increase yields. However, AI faces challenges in processing
literature data due to the unstructured format and diverse writing style of
chemical literature. To overcome these difficulties, we introduce an end-to-end
AI agent framework capable of high-fidelity extraction from extensive chemical
literature. This AI agent employs large language models (LLMs) for prompt
generation and iterative optimization. It functions as a chemistry assistant,
automating data collection and analysis, thereby saving manpower and enhancing
performance. Our framework's efficacy is evaluated using accuracy, recall, and
F1 score of reaction condition data, and we compared our method with human
experts in terms of content correctness and time efficiency. The proposed
approach marks a significant advancement in automating chemical literature
extraction and demonstrates the potential for AI to revolutionize data
management and utilization in chemistry
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