Prediction and Optimization of Ion Transport Characteristics in Nanoparticle-Based Electrolytes Using Convolutional Neural Networks

Kadulkar, Sanket; Howard, Michael P.; Truskett, Thomas M.; Ganesan, Venkat

doi:10.1021/acs.jpcb.1c02004

Cited by 13 publications

(14 citation statements)

References 61 publications

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“…Step 8: algorithm outputs the most accurate and effective bounding box result first vector. The patient body movement feature extraction network model uses YOLO algorithm to detect and locate the patient and then uses the patient body movement feature extraction network model to extract the patient feature information [ 10 ]. A convolutional neural network with three convolutional layers of different sizes is proposed to extract the body behavior characteristics of patients simultaneously.…”

Section: Methodsmentioning

confidence: 99%

A Convolutional Neural Network Algorithm for the Optimization of Emergency Nursing Rescue Efficiency for Critical Patients

Chen

Zhong

2021

Journal of Healthcare Engineering

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In order to help pathologists quickly locate the lesion area, improve the diagnostic efficiency, and reduce missed diagnosis, a convolutional neural network algorithm for the optimization of emergency nursing rescue efficiency of critical patients was proposed. Specifically, three convolution layers and convolution kernels of different sizes are used to extract the features of patients’ posture behavior, and the classifier of patients’ posture behavior recognition system is used to learn the feature information by capturing the nonlinear relationship between the features to achieve accurate classification. By testing the accuracy of patient posture behavior feature extraction, the recognition rate of a certain action, and the average recognition rate of all actions in the patient body behavior recognition system, it is proved that the convolution neural network algorithm can greatly improve the efficiency of emergency nursing. The algorithm is applied to the patient posture behavior detection system, so as to realize the identification and monitoring of patients and improve the level of intelligent medical care. Finally, the open source framework platform is used to test the patient behavior detection system. The experimental results show that the larger the test data set is, the higher the accuracy of patient posture behavior feature extraction is, and the average recognition rate of patient posture behavior category is 97.6%, thus verifying the effectiveness and correctness of the system, to prove that the convolutional neural network algorithm has a very large improvement of emergency nursing rescue efficiency.

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Section: Methodsmentioning

confidence: 99%

A Convolutional Neural Network Algorithm for the Optimization of Emergency Nursing Rescue Efficiency for Critical Patients

Chen

Zhong

2021

Journal of Healthcare Engineering

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“…This model achieved an MSE of 2.94 × 10 –5 , an R 2 value of 0.989, and an AAD (eq ) of 3.69% on the test data set. While the pore diffusion coefficient is an important property, many experimental results commonly report the diffusion ratio ( D / D 0 ), , emphasizing the effect of confinement on diffusion. In Figure b, we show the predictions of the ANN when it was retrained to predict the diffusion ratio instead of the diffusion coefficient.…”

Section: Resultsmentioning

confidence: 99%

Machine Learning Self-Diffusion Prediction for Lennard-Jones Fluids in Pores

et al. 2021

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Predicting the diffusion coefficient of fluids under nanoconfinement is important for many applications including the extraction of shale gas from kerogen and product turnover in porous catalysts. Due to the large number of important variables, including pore shape and size, fluid temperature and density, and the fluid–wall interaction strength, simulating diffusion coefficients using molecular dynamics (MD) in a systematic study could prove to be prohibitively expensive. Here, we use machine learning models trained on a subset of MD data to predict the self-diffusion coefficients of Lennard-Jones fluids in pores. Our MD data set contains 2280 simulations of ideal slit pore, cylindrical pore, and hexagonal pore geometries. We use the forward feature selection method to determine the most useful features (i.e., descriptors) for developing an artificial neutral network (ANN) model with an emphasis on easily acquired features. Our model shows good predictive ability with a coefficient of determination (i.e., R 2) of ∼0.99 and a mean squared error of ∼2.9 × 10–5. Finally, we propose an alteration to our feature set that will allow the ANN model to be applied to nonideal pore geometries.

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“…As a result, deep-learning-based approaches to dimensionality reduction for colloidal characterization have become increasingly more common. 18 , 31 , 32 , 43 − 45…”

Section: Methodsmentioning

confidence: 99%

“…Diffusion maps further do not provide an explicit functional mapping between the high- and low-dimensional spaces, thereby limiting physical interpretation of the low-dimensional space. As a result, deep-learning-based approaches to dimensionality reduction for colloidal characterization have become increasingly more common. ,,,− …”

Section: Methodsmentioning

confidence: 99%

A Deep Learning Framework Discovers Compositional Order and Self-Assembly Pathways in Binary Colloidal Mixtures

Mao

O'Leary

Mesbah

et al. 2022

JACS Au

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Binary colloidal superlattices (BSLs) have demonstrated enormous potential for the design of advanced multifunctional materials that can be synthesized via colloidal self-assembly. However, mechanistic understanding of the three-dimensional self-assembly of BSLs is largely limited due to a lack of tractable strategies for characterizing the many two-component structures that can appear during the self-assembly process. To address this gap, we present a framework for colloidal crystal structure characterization that uses branched graphlet decomposition with deep learning to systematically and quantitatively describe the self-assembly of BSLs at the single-particle level. Branched graphlet decomposition is used to evaluate local structure via high-dimensional neighborhood graphs that quantify both structural order (e.g., body-centered-cubic vs face-centered-cubic) and compositional order (e.g., substitutional defects) of each individual particle. Deep autoencoders are then used to efficiently translate these neighborhood graphs into low-dimensional manifolds from which relationships among neighborhood graphs can be more easily inferred. We demonstrate the framework on in silico systems of DNA-functionalized particles, in which two well-recognized design parameters, particle size ratio and interparticle potential well depth can be adjusted independently. The framework reveals that binary colloidal mixtures with small interparticle size disparities (i.e., A- and B-type particle radius ratios of r A / r B = 0.8 to r A / r B = 0.95) can promote the self-assembly of defect-free BSLs much more effectively than systems of identically sized particles, as nearly defect-free BCC-CsCl, FCC-CuAu, and IrV crystals are observed in the former case. The framework additionally reveals that size-disparate colloidal mixtures can undergo nonclassical nucleation pathways where BSLs evolve from dense amorphous precursors, instead of directly nucleating from dilute solution. These findings illustrate that the presented characterization framework can assist in enhancing mechanistic understanding of the self-assembly of binary colloidal mixtures, which in turn can pave the way for engineering the growth of defect-free BSLs.

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Prediction and Optimization of Ion Transport Characteristics in Nanoparticle-Based Electrolytes Using Convolutional Neural Networks

Cited by 13 publications

References 61 publications

A Convolutional Neural Network Algorithm for the Optimization of Emergency Nursing Rescue Efficiency for Critical Patients

A Convolutional Neural Network Algorithm for the Optimization of Emergency Nursing Rescue Efficiency for Critical Patients

Machine Learning Self-Diffusion Prediction for Lennard-Jones Fluids in Pores

A Deep Learning Framework Discovers Compositional Order and Self-Assembly Pathways in Binary Colloidal Mixtures

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