Using Neural Network Force Fields to Ascertain the Quality of Ab Initio Simulations of Liquid Water

2021 ◽  
Author(s):  
Alberto Torres ◽  
Luana S. Pedroza ◽  
Marivi Fernandez-Serra ◽  
Alexandre R. Rocha
Keyword(s):  
Neural Network ◽  
Ab Initio ◽  
Liquid Water ◽  
Force Fields ◽  
Ab Initio Simulations

scholarly journals Hydrogen-bond structure dynamics in bulk water: insights from ab initio simulations with coupled cluster theory

2018 ◽  
Vol 9 (8) ◽  
pp. 2065-2073 ◽  
Author(s):  
Jinfeng Liu ◽  
Xiao He ◽  
John Z. H. Zhang ◽  
Lian-Wen Qi
Keyword(s):  
Hydrogen Bond ◽  
Ab Initio ◽  
Liquid Water ◽  
Bulk Water ◽  
Coupled Cluster ◽  
Coupled Cluster Theory ◽  
Cluster Theory ◽  
Ab Initio Simulations ◽  
Structure Dynamics ◽  
Dynamical Properties

AIMD simulations using the fragment-based coupled cluster theory accurately reveal the structural and dynamical properties of liquid water.

scholarly journals Benchmarking the Computational Costs and Quality of Vibrational Spectra from Ab Initio Simulations

2021 ◽  
pp. 2100293
Author(s):  
Shima Taherivardanjani ◽  
Roman Elfgen ◽  
Werner Reckien ◽  
Estela Suarez ◽  
Eva Perlt ◽  
...  
Keyword(s):  
Ab Initio ◽  
Vibrational Spectra ◽  
Computational Costs ◽  
Ab Initio Simulations

Quantum Mechanical Interpretation of the Ultra-Low Energy Methyl-Rotation Dynamics in Porous Metal-Organic Frameworks Probed by Low-Frequency Vibrational Spectroscopy and ab initio Simulations

2018 ◽  
Author(s):  
Qi Li ◽  
Adam J. Zaczek ◽  
Timothy M. Korter ◽  
J. Axel Zeitler ◽  
Michael T. Ruggiero
Keyword(s):  
Ab Initio ◽  
Metal Organic Frameworks ◽  
Low Frequency ◽  
Rotor Dynamics ◽  
Quantum Mechanical ◽  
Valuable Insight ◽  
Void Space ◽  
Ab Initio Simulations ◽  
Metal Organic ◽  
Insight Into

<div>Understanding the nature of the interatomic interactions present within the pores of metal-organic frameworks</div><div>is critical in order to design and utilize advanced materials</div><div>with desirable applications. In ZIF-8 and its cobalt analogue</div><div>ZIF-67, the imidazolate methyl-groups, which point directly</div><div>into the void space, have been shown to freely rotate - even</div><div>down to cryogenic temperatures. Using a combination of ex-</div><div>perimental terahertz time-domain spectroscopy, low-frequency</div><div>Raman spectroscopy, and state-of-the-art ab initio simulations,</div><div>the methyl-rotor dynamics in ZIF-8 and ZIF-67 are fully charac-</div><div>terized within the context of a quantum-mechanical hindered-</div><div>rotor model. The results lend insight into the fundamental</div><div>origins of the experimentally observed methyl-rotor dynamics,</div><div>and provide valuable insight into the nature of the weak inter-</div><div>actions present within this important class of materials.</div>

Convolutional Neural Network for Customer’s Opinion on Amazon Products

2019 ◽  
Vol 8 (3) ◽  
pp. 6634-6643 ◽  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Sentiment Analysis ◽  
Latent Dirichlet Allocation ◽  
Opinion Mining ◽  
Text Documents ◽  
Customer Churn ◽  
Learning Classifier ◽  
Review Spam

Opinion mining and sentiment analysis are valuable to extract the useful subjective information out of text documents. Predicting the customer’s opinion on amazon products has several benefits like reducing customer churn, agent monitoring, handling multiple customers, tracking overall customer satisfaction, quick escalations, and upselling opportunities. However, performing sentiment analysis is a challenging task for the researchers in order to find the users sentiments from the large datasets, because of its unstructured nature, slangs, misspells and abbreviations. To address this problem, a new proposed system is developed in this research study. Here, the proposed system comprises of four major phases; data collection, pre-processing, key word extraction, and classification. Initially, the input data were collected from the dataset: amazon customer review. After collecting the data, preprocessing was carried-out for enhancing the quality of collected data. The pre-processing phase comprises of three systems; lemmatization, review spam detection, and removal of stop-words and URLs. Then, an effective topic modelling approach Latent Dirichlet Allocation (LDA) along with modified Possibilistic Fuzzy C-Means (PFCM) was applied to extract the keywords and also helps in identifying the concerned topics. The extracted keywords were classified into three forms (positive, negative and neutral) by applying an effective machine learning classifier: Convolutional Neural Network (CNN). The experimental outcome showed that the proposed system enhanced the accuracy in sentiment analysis up to 6-20% related to the existing systems.

scholarly journals Nodule Detection with Convolutional Neural Network Using Apache Spark and GPU Frameworks

2021 ◽  
Vol 11 (6) ◽  
pp. 2838
Author(s):  
Nikitha Johnsirani Venkatesan ◽  
Dong Ryeol Shin ◽  
Choon Sung Nam
Keyword(s):  
Neural Network ◽  
Radiation Dose ◽  
Convolutional Neural Network ◽  
Model Performance ◽  
Performance Comparison ◽  
Apache Spark ◽  
Training Time ◽  
Learning Framework ◽  
Proposed Model

In the pharmaceutical field, early detection of lung nodules is indispensable for increasing patient survival. We can enhance the quality of the medical images by intensifying the radiation dose. High radiation dose provokes cancer, which forces experts to use limited radiation. Using abrupt radiation generates noise in CT scans. We propose an optimal Convolutional Neural Network model in which Gaussian noise is removed for better classification and increased training accuracy. Experimental demonstration on the LUNA16 dataset of size 160 GB shows that our proposed method exhibit superior results. Classification accuracy, specificity, sensitivity, Precision, Recall, F1 measurement, and area under the ROC curve (AUC) of the model performance are taken as evaluation metrics. We conducted a performance comparison of our proposed model on numerous platforms, like Apache Spark, GPU, and CPU, to depreciate the training time without compromising the accuracy percentage. Our results show that Apache Spark, integrated with a deep learning framework, is suitable for parallel training computation with high accuracy.

scholarly journals Hybrid Reference Current Generation Theory for Solar Fed UPFC System

Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1527
Author(s):  
R. Senthil Kumar ◽  
K. Mohana Sundaram ◽  
K. S. Tamilselvan
Keyword(s):  
Neural Network ◽  
Power Factor ◽  
Reactive Power ◽  
Voltage Source ◽  
Unity Power Factor ◽  
Current Generation ◽  
Load Voltage ◽  
Reference Current Generation ◽  
Source Current

The extensive usage of power electronic components creates harmonics in the voltage and current, because of which, the quality of delivered power gets affected. Therefore, it is essential to improve the quality of power, as we reveal in this paper. The problems of load voltage, source current, and power factors are mitigated by utilizing the unified power flow controller (UPFC), in which a combination of series and shunt converters are combined through a DC-link capacitor. To retain the link voltage and to maximize the delivered power, a PV module is introduced with a high gain converter, named the switched clamped diode boost (SCDB) converter, in which the grey wolf optimization (GWO) algorithm is instigated for tracking the maximum power. To retain the link-voltage of the capacitor, the artificial neural network (ANN) is implemented. A proper control of UPFC is highly essential, which is achieved by the reference current generation with the aid of a hybrid algorithm. A genetic algorithm, hybridized with the radial basis function neural network (RBFNN), is utilized for the generation of a switching sequence, and the generated pulse has been given to both the series and shunt converters through the PWM generator. Thus, the source current and load voltage harmonics are mitigated with reactive power compensation, which results in attaining a unity power factor. The projected methodology is simulated by MATLAB and it is perceived that the total harmonic distortion (THD) of 0.84% is attained, with almost a unity power factor, and this is validated with FPGA Spartan 6E hardware.

scholarly journals AI-based localization and classification of skin disease with erythema

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ha Min Son ◽  
Wooho Jeon ◽  
Jinhyun Kim ◽  
Chan Yeong Heo ◽  
Hye Jin Yoon ◽  
...  
Keyword(s):  
Neural Network ◽  
Skin Diseases ◽  
Classification Model ◽  
Screening Tests ◽  
Sensitivity Score ◽  
Common Skin ◽  
Novel Method ◽  
Improved Performance ◽  
High Level

AbstractAlthough computer-aided diagnosis (CAD) is used to improve the quality of diagnosis in various medical fields such as mammography and colonography, it is not used in dermatology, where noninvasive screening tests are performed only with the naked eye, and avoidable inaccuracies may exist. This study shows that CAD may also be a viable option in dermatology by presenting a novel method to sequentially combine accurate segmentation and classification models. Given an image of the skin, we decompose the image to normalize and extract high-level features. Using a neural network-based segmentation model to create a segmented map of the image, we then cluster sections of abnormal skin and pass this information to a classification model. We classify each cluster into different common skin diseases using another neural network model. Our segmentation model achieves better performance compared to previous studies, and also achieves a near-perfect sensitivity score in unfavorable conditions. Our classification model is more accurate than a baseline model trained without segmentation, while also being able to classify multiple diseases within a single image. This improved performance may be sufficient to use CAD in the field of dermatology.

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