scholarly journals Predicting Critical Micelle Concentrations for Surfactants using Graph Convolutional Neural Networks

2021 ◽  
Author(s):  
Shiyi Qin ◽  
Tianyi Jin ◽  
Reid Van Lehn ◽  
Victor Zavala
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Self Assembly ◽  
High Throughput Screening ◽  
Molecular Graph ◽  
Consumer Products ◽  
Topological Information ◽  
Saliency Maps ◽  
Amphiphilic Molecules ◽  
Small Set

Surfactants are amphiphilic molecules that are widely used in consumer products, industrial processes, and biological applications. A critical property of a surfactant is the critical micelle concentration (CMC), which is the concentration at which surfactant molecules undergo cooperative self-assembly in solution. Notably, the primary method to obtain CMCs experimentally—tensiometry—is laborious and expensive. In this work, we show that graph convolutional neural networks (GCNs) can predict CMCs directly from the surfactant molecular structure. Specifically, we developed a GCN architecture that encodes the surfactant structure in the form of a molecular graph and trained it using experimental CMC data. We found that the GCN can predict CMCs with higher accuracy than previously proposed methods and that it can generalize to anionic, cationic, zwitterionic, and nonionic surfactants. Molecular saliency maps revealed how atom types and surfactant molecular substructures contribute to CMCs and found this to be in agreement with physical rules that correlate constitutional and topological information to CMCs. Following such rules, we proposed a small set of new surfactants for which experimental CMCs are not available; for these molecules, CMCs predicted with our GCN exhibited similar trends to those obtained from molecular simulations. These results provide evidence that GCNs can enable high-throughput screening of surfactants with desired self-assembly characteristics.

scholarly journals Predicting Critical Micelle Concentrations for Surfactants using Graph Convolutional Neural Networks

2021 ◽  
Author(s):  
Shiyi Qin ◽  
Reid Van Lehn ◽  
Victor Zavala ◽  
Tianyi Jin
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Self Assembly ◽  
High Throughput Screening ◽  
Molecular Graph ◽  
Consumer Products ◽  
Topological Information ◽  
Saliency Maps ◽  
Amphiphilic Molecules ◽  
Small Set

Surfactants are amphiphilic molecules that are widely used in consumer products, industrial processes, and biological applications. A critical property of a surfactant is the critical micelle concentration (CMC), which is the concentration at which surfactant molecules undergo cooperative self-assembly in solution. Notably, the primary method to obtain CMCs experimentally—tensiometry—is laborious and expensive. In this work, we show that graph convolutional neural networks (GCNs) can predict CMCs directly from the surfactant molecular structure. Specifically, we developed a GCN architecture that encodes the surfactant structure in the form of a molecular graph and trained it using experimental CMC data. We found that the GCN can predict CMCs with higher accuracy than previously proposed methods and that it can generalize to anionic, cationic, zwitterionic, and nonionic surfactants. Molecular saliency maps revealed how atom types and surfactant molecular substructures contribute to CMCs and found this to be in agreement with physical rules that correlate constitutional and topological information to CMCs. Following such rules, we proposed a small set of new surfactants for which experimental CMCs are not available; for these molecules, CMCs predicted with our GCN exhibited similar trends to those obtained from molecular simulations. These results provide evidence that GCNs can enable high-throughput screening of surfactants with desired self-assembly characteristics.

scholarly journals Predicting Critical Micelle Concentrations for Surfactants using Graph Convolutional Neural Networks

2021 ◽  
Author(s):  
Shiyi Qin ◽  
Tianyi Jin ◽  
Reid Van Lehn ◽  
Victor Zavala
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Self Assembly ◽  
High Throughput Screening ◽  
Molecular Graph ◽  
Consumer Products ◽  
Topological Information ◽  
Saliency Maps ◽  
Amphiphilic Molecules ◽  
Small Set

Surfactants are amphiphilic molecules that are widely used in consumer products, industrial processes, and biological applications. A critical property of a surfactant is the critical micelle concentration (CMC), which is the concentration at which surfactant molecules undergo cooperative self-assembly in solution. Notably, the primary method to obtain CMCs experimentally—tensiometry—is laborious and expensive. In this work, we show that graph convolutional neural networks (GCNs) can predict CMCs directly from the surfactant molecular structure. Specifically, we developed a GCN architecture that encodes the surfactant structure in the form of a molecular graph and trained it using experimental CMC data. We found that the GCN can predict CMCs with higher accuracy than previously proposed methods and that it can generalize to anionic, cationic, zwitterionic, and nonionic surfactants. Molecular saliency maps revealed how atom types and surfactant molecular substructures contribute to CMCs and found this to be in agreement with physical rules that correlate constitutional and topological information to CMCs. Following such rules, we proposed a small set of new surfactants for which experimental CMCs are not available; for these molecules, CMCs predicted with our GCN exhibited similar trends to those obtained from molecular simulations. These results provide evidence that GCNs can enable high-throughput screening of surfactants with desired self-assembly characteristics.

scholarly journals Convolutional Neural Networks for Decoding of Covert Attention Focus and Saliency Maps for EEG Feature Visualization

2019 ◽  
Author(s):  
Amr Farahat ◽  
Christoph Reichert ◽  
Catherine M. Sweeney-Reed ◽  
Hermann Hinrichs
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Cognitive Task ◽  
Event Related Potentials ◽  
Eeg Signal ◽  
Linear Discriminant ◽  
Saliency Maps ◽  
Significant Difference ◽  
Related Potentials ◽  
The Impact

ABSTRACTObjectiveConvolutional neural networks (CNNs) have proven successful as function approximators and have therefore been used for classification problems including electroencephalography (EEG) signal decoding for brain-computer interfaces (BCI). Artificial neural networks, however, are considered black boxes, because they usually have thousands of parameters, making interpretation of their internal processes challenging. Here we systematically evaluate the use of CNNs for EEG signal decoding and investigate a method for visualizing the CNN model decision process.ApproachWe developed a CNN model to decode the covert focus of attention from EEG event-related potentials during object selection. We compared the CNN and the commonly used linear discriminant analysis (LDA) classifier performance, applied to datasets with different dimensionality, and analyzed transfer learning capacity. Moreover, we validated the impact of single model components by systematically altering the model. Furthermore, we investigated the use of saliency maps as a tool for visualizing the spatial and temporal features driving the model output.Main resultsThe CNN model and the LDA classifier achieved comparable accuracy on the lower-dimensional dataset, but CNN exceeded LDA performance significantly on the higher-dimensional dataset (without hypothesis-driven preprocessing), achieving an average decoding accuracy of 90.7% (chance level = 8.3%). Parallel convolutions, tanh or ELU activation functions, and dropout regularization proved valuable for model performance, whereas the sequential convolutions, ReLU activation function, and batch normalization components, reduced accuracy or yielded no significant difference. Saliency maps revealed meaningful features, displaying the typical spatial distribution and latency of the P300 component expected during this task.SignificanceFollowing systematic evaluation, we provide recommendations for when and how to use CNN models in EEG decoding. Moreover, we propose a new approach for investigating the neural correlates of a cognitive task by training CNN models on raw high-dimensional EEG data and utilizing saliency maps for relevant feature extraction.

scholarly journals Comparing Object Recognition in Humans and Deep Convolutional Neural Networks—An Eye Tracking Study

2021 ◽  
Vol 15 ◽  
Author(s):  
Leonard Elia van Dyck ◽  
Roland Kwitt ◽  
Sebastian Jochen Denzler ◽  
Walter Roland Gruber
Keyword(s):  
Neural Networks ◽  
Object Recognition ◽  
Eye Tracking ◽  
Convolutional Neural Networks ◽  
Visual Pathway ◽  
Proof Of Concept ◽  
Deep Convolutional Neural Networks ◽  
Saliency Maps ◽  
Ventral Visual Pathway ◽  
Specific Factors

Deep convolutional neural networks (DCNNs) and the ventral visual pathway share vast architectural and functional similarities in visual challenges such as object recognition. Recent insights have demonstrated that both hierarchical cascades can be compared in terms of both exerted behavior and underlying activation. However, these approaches ignore key differences in spatial priorities of information processing. In this proof-of-concept study, we demonstrate a comparison of human observers (N = 45) and three feedforward DCNNs through eye tracking and saliency maps. The results reveal fundamentally different resolutions in both visualization methods that need to be considered for an insightful comparison. Moreover, we provide evidence that a DCNN with biologically plausible receptive field sizes called vNet reveals higher agreement with human viewing behavior as contrasted with a standard ResNet architecture. We find that image-specific factors such as category, animacy, arousal, and valence have a direct link to the agreement of spatial object recognition priorities in humans and DCNNs, while other measures such as difficulty and general image properties do not. With this approach, we try to open up new perspectives at the intersection of biological and computer vision research.

scholarly journals The analysis of VERITAS muon images using convolutional neural networks

2016 ◽  
Vol 12 (S325) ◽  
pp. 173-179 ◽  
Author(s):  
Qi Feng ◽  
Tony T. Y. Lin ◽  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Gamma Ray ◽  
Cosmic Ray ◽  
Classification Problem ◽  
Machine Learning Algorithms ◽  
Cherenkov Telescopes ◽  
Small Set ◽  
Regression Problems

AbstractImaging atmospheric Cherenkov telescopes (IACTs) are sensitive to rare gamma-ray photons, buried in the background of charged cosmic-ray (CR) particles, the flux of which is several orders of magnitude greater. The ability to separate gamma rays from CR particles is important, as it is directly related to the sensitivity of the instrument. This gamma-ray/CR-particle classification problem in IACT data analysis can be treated with the rapidly-advancing machine learning algorithms, which have the potential to outperform the traditional box-cut methods on image parameters. We present preliminary results of a precise classification of a small set of muon events using a convolutional neural networks model with the raw images as input features. We also show the possibility of using the convolutional neural networks model for regression problems, such as the radius and brightness measurement of muon events, which can be used to calibrate the throughput efficiency of IACTs.

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