scholarly journals Predicting carbon nanotube forest attributes and mechanical properties using simulated images and deep learning

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Taher Hajilounezhad ◽  
Rina Bao ◽  
Kannappan Palaniappan ◽  
Filiz Bunyak ◽  
Prasad Calyam ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Carbon Nanotube ◽  
High Throughput ◽  
Self Assembly ◽  
Mechanical Performance ◽  
Physical Parameters ◽  
Structure Property ◽  
Processing Parameter ◽  
Physics Based Simulation

AbstractUnderstanding and controlling the self-assembly of vertically oriented carbon nanotube (CNT) forests is essential for realizing their potential in myriad applications. The governing process–structure–property mechanisms are poorly understood, and the processing parameter space is far too vast to exhaustively explore experimentally. We overcome these limitations by using a physics-based simulation as a high-throughput virtual laboratory and image-based machine learning to relate CNT forest synthesis attributes to their mechanical performance. Using CNTNet, our image-based deep learning classifier module trained with synthetic imagery, combinations of CNT diameter, density, and population growth rate classes were labeled with an accuracy of >91%. The CNTNet regression module predicted CNT forest stiffness and buckling load properties with a lower root-mean-square error than that of a regression predictor based on CNT physical parameters. These results demonstrate that image-based machine learning trained using only simulated imagery can distinguish subtle CNT forest morphological features to predict physical material properties with high accuracy. CNTNet paves the way to incorporate scanning electron microscope imagery for high-throughput material discovery.

scholarly journals Evaluation of process-structure-property relationships of carbon nanotube forests using simulation and deep learning

2020 ◽  
Author(s):  
◽  
Taheg Hajilounezhad
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Carbon Nanotube ◽  
Forest Growth ◽  
Structure Property ◽  
Deposition Parameter ◽  
Structure Property Relationships ◽  
In Situ Experiments ◽  
Process Structure

This work is aimed to explore process-structure-property relationships of carbon nanotube (CNT) forests. CNTs have superior mechanical, electrical and thermal properties that make them suitable for many applications. Yet, due to lack of manufacturing control, there is a huge performance gap between promising properties of individual CNTs and CNT forest properties that hinders their adoption into potential industrial applications. In this research, computational modelling, in-situ electron microscopy for CNT synthesis, and data-driven and high-throughput deep convolutional neural networks are employed to not only accelerate implementing CNTs in various applications but also to establish a framework to make validated predictive models that can be easily extended to achieve application-tailored synthesis of any materials. A time-resolved and physics-based finite-element simulation tool is modelled in MATLAB to investigate synthesis of CNT forests, specially to study the CNT-CNT interactions and generated mechanical forces and their role in ensemble structure and properties. A companion numerical model with similar construct is then employed to examine forest mechanical properties in compression. In addition, in-situ experiments are carried out inside Environmental Scanning Electron Microscope (ESEM) to nucleate and synthesize CNTs. Findings may primarily be used to expand the forest growth and self-assembly knowledge and to validate the assumptions of simulation package. Also, SEM images can be used as feed database to construct a deep learning model to grow CNTs by design. The chemical vapor deposition parameter space of CNT synthesis is so vast that it is not possible to investigate all conceivable combinations in terms of time and costs. Hence, simulated CNT forest morphology images are used to train machine learning and learning algorithms that are able to predict CNT synthesis conditions based on desired properties. Exceptionally high prediction accuracies of R2 > 0.94 is achieved for buckling load and stiffness, as well as accuracies of > 0.91 for the classification task. This high classification accuracy promotes discovering the CNT forest synthesis-structure relationships so that their promising performance can be adopted in real world applications. We foresee this work as a meaningful step towards creating an unsupervised simulation using machine learning techniques that can seek out the desired CNT forest synthesis parameters to achieve desired property sets for diverse applications.

scholarly journals Application of Bioactivity Profile Based Fingerprints for Building Machine Learning Models

2018 ◽  
Author(s):  
Noé Sturm ◽  
Jiangming Sun ◽  
Yves Vandriessche ◽  
Andreas Mayr ◽  
Günter Klambauer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
High Throughput ◽  
Scaffold Hopping ◽  
Learning Models ◽  
Industrial Data ◽  
Structural Descriptors ◽  
Bioactivity Profile ◽  
Machine Learning Models

<div>This article describes an application of high-throughput fingerprints (HTSFP) built upon industrial data accumulated over the years. </div><div>The fingerprint was used to build machine learning models (multi-task deep learning + SVM) for compound activity predictions towards a panel of 131 targets. </div><div>Quality of the predictions and the scaffold hopping potential of the HTSFP were systematically compared to traditional structural descriptors ECFP. </div><div><br></div>

scholarly journals PARROT is a flexible recurrent neural network framework for analysis of large protein datasets

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Daniel Griffith ◽  
Alex S Holehouse
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
High Throughput ◽  
Recurrent Neural Network ◽  
Transcriptional Activation ◽  
Network Architecture ◽  
Learning Approaches ◽  
Large Protein ◽  
Protein Datasets

The rise of high-throughput experiments has transformed how scientists approach biological questions. The ubiquity of large-scale assays that can test thousands of samples in a day has necessitated the development of new computational approaches to interpret this data. Among these tools, machine learning approaches are increasingly being utilized due to their ability to infer complex nonlinear patterns from high-dimensional data. Despite their effectiveness, machine learning (and in particular deep learning) approaches are not always accessible or easy to implement for those with limited computational expertise. Here we present PARROT, a general framework for training and applying deep learning-based predictors on large protein datasets. Using an internal recurrent neural network architecture, PARROT is capable of tackling both classification and regression tasks while only requiring raw protein sequences as input. We showcase the potential uses of PARROT on three diverse machine learning tasks: predicting phosphorylation sites, predicting transcriptional activation function of peptides generated by high-throughput reporter assays, and predicting the fibrillization propensity of amyloid beta with data generated by deep mutational scanning. Through these examples, we demonstrate that PARROT is easy to use, performs comparably to state-of-the-art computational tools, and is applicable for a wide array of biological problems.

scholarly journals Outdoor Plant Segmentation With Deep Learning for High-Throughput Field Phenotyping on a Diverse Wheat Dataset

2022 ◽  
Vol 12 ◽  
Author(s):  
Radek Zenkl ◽  
Radu Timofte ◽  
Norbert Kirchgessner ◽  
Lukas Roth ◽  
Andreas Hund ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
High Throughput ◽  
Developmental Stages ◽  
Agricultural Practices ◽  
Support Vector ◽  
Learning Approaches ◽  
Support Vector Classifier ◽  
Wide Range ◽  
Field Phenotyping

Robust and automated segmentation of leaves and other backgrounds is a core prerequisite of most approaches in high-throughput field phenotyping. So far, the possibilities of deep learning approaches for this purpose have not been explored adequately, partly due to a lack of publicly available, appropriate datasets. This study presents a workflow based on DeepLab v3+ and on a diverse annotated dataset of 190 RGB (350 x 350 pixels) images. Images of winter wheat plants of 76 different genotypes and developmental stages have been acquired throughout multiple years at high resolution in outdoor conditions using nadir view, encompassing a wide range of imaging conditions. Inconsistencies of human annotators in complex images have been quantified, and metadata information of camera settings has been included. The proposed approach achieves an intersection over union (IoU) of 0.77 and 0.90 for plants and soil, respectively. This outperforms the benchmarked machine learning methods which use Support Vector Classifier and/or Random Forrest. The results show that a small but carefully chosen and annotated set of images can provide a good basis for a powerful segmentation pipeline. Compared to earlier methods based on machine learning, the proposed method achieves better performance on the selected dataset in spite of using a deep learning approach with limited data. Increasing the amount of publicly available data with high human agreement on annotations and further development of deep neural network architectures will provide high potential for robust field-based plant segmentation in the near future. This, in turn, will be a cornerstone of data-driven improvement in crop breeding and agricultural practices of global benefit.

scholarly journals GPTransformer: A Transformer-Based Deep Learning Method for Predicting Fusarium Related Traits in Barley

2021 ◽  
Vol 12 ◽  
Author(s):  
Sheikh Jubair ◽  
James R. Tucker ◽  
Nathan Henderson ◽  
Colin W. Hiebert ◽  
Ana Badea ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Molecular Markers ◽  
Fusarium Head Blight ◽  
High Throughput ◽  
Genomic Prediction ◽  
Prediction Models ◽  
Spring Barley ◽  
Pearson Correlation ◽  
Head Blight

Fusarium head blight (FHB) incited by Fusarium graminearum Schwabe is a devastating disease of barley and other cereal crops worldwide. Fusarium head blight is associated with trichothecene mycotoxins such as deoxynivalenol (DON), which contaminates grains, making them unfit for malting or animal feed industries. While genetically resistant cultivars offer the best economic and environmentally responsible means to mitigate disease, parent lines with adequate resistance are limited in barley. Resistance breeding based upon quantitative genetic gains has been slow to date, due to intensive labor requirements of disease nurseries. The production of a high-throughput genome-wide molecular marker assembly for barley permits use in development of genomic prediction models for traits of economic importance to this crop. A diverse panel consisting of 400 two-row spring barley lines was assembled to focus on Canadian barley breeding programs. The panel was evaluated for FHB and DON content in three environments and over 2 years. Moreover, it was genotyped using an Illumina Infinium High-Throughput Screening (HTS) iSelect custom beadchip array of single nucleotide polymorphic molecular markers (50 K SNP), where over 23 K molecular markers were polymorphic. Genomic prediction has been demonstrated to successfully reduce FHB and DON content in cereals using various statistical models. Herein, we have studied an alternative method based on machine learning and compare it with a statistical approach. The bi-allelic SNPs represented pairs of alleles and were encoded in two ways: as categorical (–1, 0, 1) or using Hardy-Weinberg probability frequencies. This was followed by selecting essential genomic markers for phenotype prediction. Subsequently, a Transformer-based deep learning algorithm was applied to predict FHB and DON. Apart from the Transformer method, a Residual Fully Connected Neural Network (RFCNN) was also applied. Pearson correlation coefficients were calculated to compare true vs. predicted outputs. Models which included all markers generally showed marginal improvement in prediction. Hardy-Weinberg encoding generally improved correlation for FHB (6.9%) and DON (9.6%) for the Transformer network. This study suggests the potential of the Transformer based method as an alternative to the popular BLUP model for genomic prediction of complex traits such as FHB or DON, having performed equally or better than existing machine learning and statistical methods.

scholarly journals Design of self-assembly dipeptide hydrogels and machine learning via their chemical features

2019 ◽  
Vol 116 (23) ◽  
pp. 11259-11264 ◽  
Author(s):  
Fei Li ◽  
Jinsong Han ◽  
Tian Cao ◽  
William Lam ◽  
Baoer Fan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Self Assembly ◽  
Biomedical Applications ◽  
Structure Property ◽  
Chemical Structures ◽  
Support Cell ◽  
Applied Machine Learning ◽  
Machine Learning Approach ◽  
Structure Property Relationship ◽  
Assembly Behavior

Hydrogels that are self-assembled by peptides have attracted great interest for biomedical applications. However, the link between chemical structures of peptides and their corresponding hydrogel properties is still unclear. Here, we showed a combinational approach to generate a structurally diverse hydrogel library with more than 2,000 peptides and evaluated their corresponding properties. We used a quantitative structure–property relationship to calculate their chemical features reflecting the topological and physicochemical properties, and applied machine learning to predict the self-assembly behavior. We observed that the stiffness of hydrogels is correlated with the diameter and cross-linking degree of the nanofiber. Importantly, we demonstrated that the hydrogels support cell proliferation in culture, suggesting the biocompatibility of the hydrogel. The combinatorial hydrogel library and the machine learning approach we developed linked the chemical structures with their self-assembly behavior and can accelerate the design of novel peptide structures for biomedical use.

scholarly journals Combining machine learning and high-throughput experimentation to discover photocatalytically active organic molecules

2021 ◽  
Author(s):  
Xiaobo Li ◽  
Phillip M. Maffettone ◽  
Yu Che ◽  
Tao Liu ◽  
Linjiang Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Throughput ◽  
Organic Molecules ◽  
Chemical Compounds ◽  
Structure Property ◽  
Design Rules ◽  
Useful Components ◽  
High Throughput Experimentation ◽  
Unique Chemical

Light-absorbing organic molecules are useful components in photocatalysts, but it is difficult to formulate reliable structure-property design rules. More than 100 million unique chemical compounds are documented in the PubChem...

scholarly journals Exploration of Carbon Nanotube Forest Synthesis-Structure Relationships Using Physics-Based Simulation and Machine Learning

2020 ◽  
Author(s):  
Taher Hajilounezhad ◽  
Zakariya A. Oraibi ◽  
Ramakrishna Surya ◽  
Filiz Bunyak ◽  
Matthew R. Maschmann ◽  
...  
Keyword(s):  
Machine Learning ◽  
Self Assembly ◽  
Growth Parameters ◽  
Synthesis Process ◽  
Design Development ◽  
Physical Processes ◽  
Structural Morphology ◽  
Class Labels ◽  
Physics Based Simulation ◽  
Process Structure

The parameter space of CNT forest synthesis is vastand multidimensional, making experimental and/or numericalexploration of the synthesis prohibitive. We propose a morepractical approach to explore the synthesis-process relationshipsof CNT forests using machine learning (ML) algorithms toinfer the underlying complex physical processes. Currently, nosuch ML model linking CNT forest morphology to synthesisparameters has been demonstrated. In the current work, weuse a physics-based numerical model to generate CNT forestmorphology images with known synthesis parameters to trainsuch a ML algorithm. The CNT forest synthesis variablesof CNT diameter and CNT number densities are varied togenerate a total of 12 distinct CNT forest classes. Images of theresultant CNT forests at different time steps during the growthand self-assembly process are then used as the training dataset.Based on the CNT forest structural morphology, multiplesingle and combined histogram-based texture descriptors areused as features to build a random forest (RF) classifier topredict class labels based on correlation of CNT forest physicalattributes with the growth parameters. The machine learningmodel achieved an accuracy of up to 83.5% on predicting thesynthesis conditions of CNT number density and diameter.These results are the first step towards rapidly characterizingCNT forest attributes using machine learning. Identifying therelevant process-structure interactions for the CNT forests usingphysics-based simulations and machine learning could rapidlyadvance the design, development, and adoption of CNT forestapplications with varied morphologies and properties.

scholarly journals Machine Learning-Assisted High-Throughput Molecular Dynamics Simulation of High-Mechanical Performance Carbon Nanotube Structure

Nanomaterials ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 2459
Author(s):  
Yi Xiang ◽  
Koji Shimoyama ◽  
Keiichi Shirasu ◽  
Go Yamamoto
Keyword(s):  
Machine Learning ◽  
Molecular Dynamics ◽  
Tensile Strength ◽  
High Throughput ◽  
Crosslink Density ◽  
Mechanical Performance ◽  
Dynamics Simulation ◽  
Optimal Structure ◽  
Outer Diameter ◽  
Nominal Tensile Strength

Carbon nanotubes (CNTs) are novel materials with extraordinary mechanical properties. To gain insight on the design of high-mechanical-performance CNT-reinforced composites, the optimal structure of CNTs with high nominal tensile strength was determined in this study, where the nominal values correspond to the cross-sectional area of the entire specimen, including the hollow core. By using machine learning-assisted high-throughput molecular dynamics (HTMD) simulation, the relationship among the following structural parameters/properties was investigated: diameter, number of walls, chirality, and crosslink density. A database, comprising the various tensile test simulation results, was analyzed using a self-organizing map (SOM). It was observed that the influence of crosslink density on the nominal tensile strength tends to gradually decrease from the outside to the inside; generally, the crosslink density between the outermost wall and its adjacent wall is highly significant. In particular, based on our calculation conditions, five-walled, armchair-type CNTs with an outer diameter of 43.39 Å and crosslink densities (between the inner wall and outer wall) of 1.38 ± 1.16%, 1.13 ± 0.69%, 1.54 ± 0.57%, and 1.36 ± 0.35% were believed to be the optimal structure, with the nominal tensile strength and nominal Young’s modulus reaching approximately 58–64 GPa and 677–698 GPa.

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