scholarly journals Corrections of Molecular Morphology and Hydrogen Bond for Improved Crystal Density Prediction

Molecules ◽  
2019 ◽  
Vol 25 (1) ◽  
pp. 161 ◽  
Author(s):  
Linyuan Wang ◽  
Miao Zhang ◽  
Jie Chen ◽  
Liang Su ◽  
Shicao Zhao ◽  
...  
Keyword(s):  
Hydrogen Bond ◽  
Energetic Materials ◽  
Prediction Models ◽  
Molecular Design ◽  
Small Data ◽  
Detonation Pressure ◽  
Data Set ◽  
Density Prediction ◽  
Crystal Density ◽  
Molecular Morphology

Density prediction is of great significance for molecular design of energetic materials, since detonation velocity linearly with density and detonation pressure increases with the density squared. However, the accuracy and generalization of former reported prediction models need further improvement, because most of them are derived from small data sets and few molecular descriptors. As shown in this paper, for molecules presenting brick-like shape or containing more hydrogen-bond donors the predicted densities have large negative deviations from experimental values. Thus, a molecular morphology descriptor η and a hydrogen-bond descriptor Hb are introduced as correction items to build 3 new QSPR models. Besides, 3694 nitro compounds are adopted as data set by this work. The accuracies are obviously improved, and the generalizations are verified by an independent test set. At the level of B3PW91/6-31G(d,p), the effective ratios (ERs) of the 3 Equations, for Δρ < 5%, are 92.7%, 91.8%, and 93.3%; for Δρ < 2%, the values are 53.5%, 51.3%, and 54.7%. At the level of B3LYP/6-31G**, for Δρ < 5%, the values are 92.3%, 91.4% and 92.9%; for Δρ < 2%, the values are 53.7%, 51.4% and 53.2%.

scholarly journals Applying Machine Learning Techniques to Predict the Properties of Energetic Materials

2018 ◽  
Author(s):  
Daniel Elton ◽  
Zois Boukouvalas ◽  
Mark S. Butrico ◽  
Mark D. Fuge ◽  
Peter W. Chung
Keyword(s):  
Machine Learning ◽  
Energetic Materials ◽  
Molecular Structures ◽  
Machine Learning Techniques ◽  
Small Data ◽  
Detonation Pressure ◽  
Learning Models ◽  
Data Set ◽  
Learning Techniques ◽  
Machine Learning Models

We present a proof of concept that machine learning techniques can be used to predict the properties of CNOHF energetic molecules from their molecular structures. We focus on a small but diverse dataset consisting of 109 molecular structures spread across ten compound classes. Up until now, candidate molecules for energetic materials have been screened using predictions from expensive quantum simulations and thermochemical codes. We present a comprehensive comparison of machine learning models and several molecular featurization methods - sum over bonds, custom descriptors, Coulomb matrices, bag of bonds, and fingerprints. The best featurization was sum over bonds (bond counting), and the best model was kernel ridge regression. Despite having a small data set, we obtain acceptable errors and Pearson correlations for the prediction of detonation pressure, detonation velocity, explosive energy, heat of formation, density, and other properties out of sample. By including another dataset with 309 additional molecules in our training we show how the error can be pushed lower, although the convergence with number of molecules is slow. Our work paves the way for future applications of machine learning in this domain, including automated lead generation and interpreting machine learning models to obtain novel chemical insights.

scholarly journals Applying Machine Learning Techniques to Predict the Properties of Energetic Materials

2018 ◽  
Author(s):  
Daniel Elton ◽  
Zois Boukouvalas ◽  
Mark S. Butrico ◽  
Mark D. Fuge ◽  
Peter W. Chung
Keyword(s):  
Machine Learning ◽  
Energetic Materials ◽  
Molecular Structures ◽  
Machine Learning Techniques ◽  
Small Data ◽  
Detonation Pressure ◽  
Learning Models ◽  
Data Set ◽  
Learning Techniques ◽  
Machine Learning Models

We present a proof of concept that machine learning techniques can be used to predict the properties of CNOHF energetic molecules from their molecular structures. We focus on a small but diverse dataset consisting of 109 molecular structures spread across ten compound classes. Up until now, candidate molecules for energetic materials have been screened using predictions from expensive quantum simulations and thermochemical codes. We present a comprehensive comparison of machine learning models and several molecular featurization methods - sum over bonds, custom descriptors, Coulomb matrices, Bag of Bonds, and fingerprints. The best featurization was sum over bonds (bond counting), and the best model was kernel ridge regression. Despite having a small data set, we obtain acceptable errors and Pearson correlations for the prediction of detonation pressure, detonation velocity, explosive energy, heat of formation, density, and other properties out of sample. By including another dataset with 309 additional molecules in our training we show how the error can be pushed lower, although the convergence with number of molecules is slow. Our work paves the way for future applications of machine learning in this domain, including automated lead generation and interpreting machine learning models to obtain novel chemical insights.

scholarly journals Applying Machine Learning Techniques to Predict the Properties of Energetic Materials

2018 ◽  
Author(s):  
Daniel Elton ◽  
Zois Boukouvalas ◽  
Mark S. Butrico ◽  
Mark D. Fuge ◽  
Peter W. Chung
Keyword(s):  
Machine Learning ◽  
Energetic Materials ◽  
Molecular Structures ◽  
Machine Learning Techniques ◽  
Small Data ◽  
Detonation Pressure ◽  
Learning Models ◽  
Data Set ◽  
Learning Techniques ◽  
Machine Learning Models

We present a proof of concept that machine learning techniques can be used to predict the properties of CNOHF energetic molecules from their molecular structures. We focus on a small but diverse dataset consisting of 109 molecular structures spread across ten compound classes. Up until now, candidate molecules for energetic materials have been screened using predictions from expensive quantum simulations and thermochemical codes. We present a comprehensive comparison of machine learning models and several molecular featurization methods - sum over bonds, custom descriptors, Coulomb matrices, bag of bonds, and fingerprints. The best featurization was sum over bonds (bond counting), and the best model was kernel ridge regression. Despite having a small data set, we obtain acceptable errors and Pearson correlations for the prediction of detonation pressure, detonation velocity, explosive energy, heat of formation, density, and other properties out of sample. By including another dataset with 309 additional molecules in our training we show how the error can be pushed lower, although the convergence with number of molecules is slow. Our work paves the way for future applications of machine learning in this domain, including automated lead generation and interpreting machine learning models to obtain novel chemical insights.

scholarly journals Coupling a weather model directly to GNSS orbit determination

2021 ◽  
Author(s):  
Angel Navarro Trastoy ◽  
Sebastian Strasser ◽  
Lauri Tuppi ◽  
Maksym Vasiuta ◽  
Markku Poutanen ◽  
...  
Keyword(s):  
Orbit Determination ◽  
Prediction Models ◽  
Weather Prediction ◽  
Direct Coupling ◽  
Atmospheric Effects ◽  
Satellite Orbits ◽  
Small Data ◽  
Neutral Atmosphere ◽  
Data Set ◽  
Weather Model

Abstract. Neutral atmosphere bends and delays propagation of microwave signals in satellite-based navigation. Weather prediction models can be used to estimate these effects by providing 3-dimensional refraction fields to estimate signal delay in the zenith direction and determine a low-dimensional mapping of this delay to desired azimuth and elevation angles. In this study, a global numerical weather prediction model (OpenIFS licensed for Academic use by ECMWF) is used to generate the refraction fields. The ray-traced slant delays are supplied as such – in contrast to mapping – for an orbit solver (GROOPS software toolkit of TUG) which applies the raw observation method. Here we show that such a close coupling is possible without need for major additional modifications in the solver codes. The main finding here is that the adopted approach provides a very good a priori model for the atmospheric effects on navigation signals, as measured with the midnight discontinuity of GNSS satellite orbits. Our interpretation is that removal of the intermediate mapping step allows to take advantage of the local refraction field asymmetries in the GNSS signal processing. Moreover, the direct coupling helps in identifying deficiencies in the slant delay computation because the modelling errors are not convoluted in the precision-reducing mapping. These conclusions appear robust, despite the relatively small data set of raw code and phase observations covering the core network of 66 ground-based stations of the International GNSS Service over one-month periods in December 2016 and June 2017. More generally, the new configuration enhances our control of geodetic and meteorological aspects of the orbit problem. This is pleasant because we can, for instance, regulate at will the weather model output frequency and increase coverage of spatio-temporal aspects of weather variations. The direct coupling of a weather model in precise GNSS orbit determination presented in this paper provides a unique framework for benefiting even more widely than previously the apparent synergies in space geodesy and meteorology.

scholarly journals Screening for energetic compounds based on 1,3-dinitrohexahydropyrimidine skeleton and 5-various explosopheres: molecular design and computational study

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Binghui Duan ◽  
Ning Liu ◽  
Xianming Lu ◽  
Hongchang Mo ◽  
Qian Zhang ◽  
...  
Keyword(s):  
Thermal Stability ◽  
Energetic Materials ◽  
Molecular Design ◽  
Computational Study ◽  
Impact Sensitivity ◽  
Detonation Properties ◽  
Detonation Pressure ◽  
Energetic Compounds ◽  
Good Thermal Stability ◽  
Energetic Performance

Abstract In this paper, twelve 1,3-dinitrohexahydropyrimidine-based energetic compounds were designed by introducing various explosopheres into hexahydropyrimidine skeleton. Their geometric and electronic structures, heats of formation (HOFs), energetic performance, thermal stability and impact sensitivity were discussed. It is found that the incorporation of electron-withdrawing groups (–NO2, –NHNO2, –N3, –CH(NO2)2, –CF(NO2)2, –C(NO2)3) improves HOFs of the derivatives and all the substituents contribute to enhancing the densities and detonation properties (D, P) of the title compounds. Therein, the substitution of –C(NO2)3 features the best energetic performance with detonation velocity of 9.40 km s−1 and detonation pressure of 40.20 GPa. An analysis of the bond dissociation energies suggests that N–NO2 bond may be the initial site in the thermal decompositions for most of the derivatives. Besides, –ONO2 and –NF2 derivatives stand out with lower impact sensitivity. Characters with striking detonation properties (D = 8.62 km s−1, P = 35.08 GPa; D = 8.81 km s−1, P = 34.88 GPa), good thermal stability, and acceptable impact sensitivity (characteristic height H50 over 34 cm) lead novel compounds 5,5-difluoramine-1,3-dinitrohexahydropyrimidine (K) and 5-fluoro-1,3,5-trinitrohexahydropyrimidine (L) to be very promising energetic materials. This work provides the theoretical molecular design and a reasonable synthetic route of L for further experimental synthesis and testing.

In silico Prediction of Inhibitory Constant of Thrombin Inhibitors Using Machine Learning

2019 ◽  
Vol 21 (9) ◽  
pp. 662-669 ◽  
Author(s):  
Junnan Zhao ◽  
Lu Zhu ◽  
Weineng Zhou ◽  
Lingfeng Yin ◽  
Yuchen Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Regression Tree ◽  
Large Data ◽  
Thrombin Inhibitors ◽  
Coagulation Cascade ◽  
Gradient Boosting ◽  
Support Vector ◽  
Data Set ◽  
Descriptor Selection

Background: Thrombin is the central protease of the vertebrate blood coagulation cascade, which is closely related to cardiovascular diseases. The inhibitory constant Ki is the most significant property of thrombin inhibitors. Method: This study was carried out to predict Ki values of thrombin inhibitors based on a large data set by using machine learning methods. Taking advantage of finding non-intuitive regularities on high-dimensional datasets, machine learning can be used to build effective predictive models. A total of 6554 descriptors for each compound were collected and an efficient descriptor selection method was chosen to find the appropriate descriptors. Four different methods including multiple linear regression (MLR), K Nearest Neighbors (KNN), Gradient Boosting Regression Tree (GBRT) and Support Vector Machine (SVM) were implemented to build prediction models with these selected descriptors. Results: The SVM model was the best one among these methods with R2=0.84, MSE=0.55 for the training set and R2=0.83, MSE=0.56 for the test set. Several validation methods such as yrandomization test and applicability domain evaluation, were adopted to assess the robustness and generalization ability of the model. The final model shows excellent stability and predictive ability and can be employed for rapid estimation of the inhibitory constant, which is full of help for designing novel thrombin inhibitors.

The Lyapunov Exponent Variance of an Electronic Manufacturing Enterprise’s Daily Qualified Rate Time Series by Improved Small Data Sets Approach

2012 ◽  
Vol 197 ◽  
pp. 271-277
Author(s):  
Zhu Ping Gong
Keyword(s):  
Time Series ◽  
Lyapunov Exponent ◽  
Chaotic Time Series ◽  
Quality System ◽  
Quality Level ◽  
Largest Lyapunov Exponent ◽  
Small Data ◽  
Data Set ◽  
Electronic Manufacturing ◽  
Small Data Set

Small data set approach is used for the estimation of Largest Lyapunov Exponent (LLE). Primarily, the mean period drawback of Small data set was corrected. On this base, the LLEs of daily qualified rate time series of HZ, an electronic manufacturing enterprise, were estimated and all positive LLEs were taken which indicate that this time series is a chaotic time series and the corresponding produce process is a chaotic process. The variance of the LLEs revealed the struggle between the divergence nature of quality system and quality control effort. LLEs showed sharp increase in getting worse quality level coincide with the company shutdown. HZ’s daily qualified rate, a chaotic time series, shows us the predictable nature of quality system in a short-run.

scholarly journals Chalcogen Bond versus Weak Hydrogen Bond: Changing Contributions in Determining the Crystal Packing of [1,2,5]-Chalcogenadiazole-Fused Tetracyanonaphthoquinodimethanes

2021 ◽  
Vol 03 (02) ◽  
pp. 090-096
Author(s):  
Yusuke Ishigaki ◽  
Kota Asai ◽  
Takuya Shimajiri ◽  
Tomoyuki Akutagawa ◽  
Takanori Fukushima ◽  
...  
Keyword(s):  
Hydrogen Bond ◽  
Density Functional ◽  
Molecular Design ◽  
Crystal Packing ◽  
Cyano Group ◽  
Density Functional Theory Calculations ◽  
Weak Hydrogen Bond ◽  
Functional Theory ◽  
Chalcogen Bond ◽  
Thiadiazole Ring

The crystal structures of a series of tetracyanonaphthoquinodimethanes fused with a selenadiazole or thiadiazole ring revealed that their molecular packing is determined mainly by two intermolecular interactions: chalcogen bond (ChB) and weak hydrogen bond (WHB). ChB between Se and a cyano group dictates the packing of selenadiazole derivatives, whereas the S-based ChB is much weaker and competes with WHB in thiadiazole analogues. This difference can be explained by different electrostatic potentials as revealed by density functional theory calculations. A proper molecular design that weakens WHB can change the contribution of ChB in determining the crystal packing of thiadiazole derivatives.

scholarly journals Classification of jujube defects in small data sets based on transfer learning

2021 ◽  
Author(s):  
Jianping Ju ◽  
Hong Zheng ◽  
Xiaohang Xu ◽  
Zhongyuan Guo ◽  
Zhaohui Zheng ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Loss Function ◽  
Training Model ◽  
Parameter Distribution ◽  
Test Accuracy ◽  
Small Data ◽  
Data Sets ◽  
Data Set ◽  
Small Data Sets

AbstractAlthough convolutional neural networks have achieved success in the field of image classification, there are still challenges in the field of agricultural product quality sorting such as machine vision-based jujube defects detection. The performance of jujube defect detection mainly depends on the feature extraction and the classifier used. Due to the diversity of the jujube materials and the variability of the testing environment, the traditional method of manually extracting the features often fails to meet the requirements of practical application. In this paper, a jujube sorting model in small data sets based on convolutional neural network and transfer learning is proposed to meet the actual demand of jujube defects detection. Firstly, the original images collected from the actual jujube sorting production line were pre-processed, and the data were augmented to establish a data set of five categories of jujube defects. The original CNN model is then improved by embedding the SE module and using the triplet loss function and the center loss function to replace the softmax loss function. Finally, the depth pre-training model on the ImageNet image data set was used to conduct training on the jujube defects data set, so that the parameters of the pre-training model could fit the parameter distribution of the jujube defects image, and the parameter distribution was transferred to the jujube defects data set to complete the transfer of the model and realize the detection and classification of the jujube defects. The classification results are visualized by heatmap through the analysis of classification accuracy and confusion matrix compared with the comparison models. The experimental results show that the SE-ResNet50-CL model optimizes the fine-grained classification problem of jujube defect recognition, and the test accuracy reaches 94.15%. The model has good stability and high recognition accuracy in complex environments.

scholarly journals 2D–3D reconstruction of distal forearm bone from actual X-ray images of the wrist using convolutional neural networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ryoya Shiode ◽  
Mototaka Kabashima ◽  
Yuta Hiasa ◽  
Kunihiro Oka ◽  
Tsuyoshi Murase ◽  
...  
Keyword(s):  
Wrist Joint ◽  
High Accuracy ◽  
Training Data ◽  
Small Data ◽  
Data Set ◽  
Accuracy Estimation ◽  
X Ray ◽  
Learning Network ◽  
Forearm Bone ◽  
Deep Learning Network

AbstractThe purpose of the study was to develop a deep learning network for estimating and constructing highly accurate 3D bone models directly from actual X-ray images and to verify its accuracy. The data used were 173 computed tomography (CT) images and 105 actual X-ray images of a healthy wrist joint. To compensate for the small size of the dataset, digitally reconstructed radiography (DRR) images generated from CT were used as training data instead of actual X-ray images. The DRR-like images were generated from actual X-ray images in the test and adapted to the network, and high-accuracy estimation of a 3D bone model from a small data set was possible. The 3D shape of the radius and ulna were estimated from actual X-ray images with accuracies of 1.05 ± 0.36 and 1.45 ± 0.41 mm, respectively.

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