scholarly journals HOMO–LUMO Gaps and Molecular Structures of Polycyclic Aromatic Hydrocarbons in Soot Formation

2021 ◽  
Vol 7 ◽  
Author(s):  
Yabei Xu ◽  
Qingzhao Chu ◽  
Dongping Chen ◽  
Andrés Fuentes
Keyword(s):  
Machine Learning ◽  
Functional Groups ◽  
Soot Formation ◽  
Absolute Error ◽  
Learning Model ◽  
Molecular Structures ◽  
Machine Learning Model ◽  
The Impact ◽  
Homo Lumo ◽  
Gap Values

A large number of PAH molecules is collected from recent literature. The HOMO-LUMO gap value of PAHs was computed at the level of B3LYP/6-311+G (d,p). The gap values lie in the range of 0.64–6.59 eV. It is found that the gap values of all PAH molecules exhibit a size dependency to some extent. However, the gap values may show a big variation even at the same size due to the complexity in the molecular structure. All collected PAHs are further classified into seven groups according to features in the structures, including the types of functional groups and the molecular planarity. The impact of functional groups, including –OH, –CHO, –COOH, =O, –O– and –CnHm on the bandgap is discussed in detail. The substitution of ketone group has the greatest reduction on the HOMO-LUMO gap of PAH molecules. Besides functional groups, we found that both local structure and the position of five-member rings make critical impacts on the bandgap via a detailed analysis of featured PAHs with unexpected low and high gap values. Among all these factors, the five-member rings forming nonplanar PAHs impact the gap most. Furthermore, we developed a machine learning model to predict the HOMO-LUMO gaps of PAHs, and the average absolute error is only 0.19 eV compared with the DFT calculations. The excellent performance of the machine learning model provides us an accurate and efficient way to explore the band information of PAHs in soot formation.

scholarly journals Author Correction: Machine learning model to project the impact of COVID-19 on US motor gasoline demand

Nature Energy ◽  
2020 ◽  
Vol 5 (12) ◽  
pp. 1051-1052
Author(s):  
Shiqi Ou ◽  
Xin He ◽  
Weiqi Ji ◽  
Wei Chen ◽  
Lang Sui ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Model ◽  
Gasoline Demand ◽  
Motor Gasoline ◽  
Machine Learning Model ◽  
The Impact

scholarly journals BonDNet: A Graph Neural Network for the Prediction of Bond Dissociation Energies for Charged Molecules

2020 ◽  
Author(s):  
Mingjian Wen ◽  
Samuel Blau ◽  
Evan Spotte-Smith ◽  
Shyam Dwaraknath ◽  
Kristin Persson
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Chemical Reactivity ◽  
Absolute Error ◽  
Learning Model ◽  
Chemical Processes ◽  
Accurate Prediction ◽  
Global Features ◽  
Bond Dissociation ◽  
Machine Learning Model

<div><div><div><p>A broad collection of technologies, including e.g. drug metabolism, biofuel combustion, photochemical decontamination of water, and interfacial passivation in energy production/storage systems rely on chemical processes that involve bond-breaking molecular reactions. In this context, a fundamental thermodynamic property of interest is the bond dissociation energy (BDE) which measures the strength of a chemical bond. Fast and accurate prediction of BDEs for arbitrary molecules would lay the groundwork for data-driven projections of complex reaction cascades and hence a deeper understanding of these critical chemical processes and, ultimately, how to reverse design them. In this paper, we propose a chemically inspired graph neural network machine learning model, BonDNet, for the rapid and accurate prediction of BDEs. BonDNet maps the difference between the molecular representations of the reactants and products to the reaction BDE. Because of the use of this difference representation and the introduction of global features, including molecular charge, it is the first machine learning model capable of predicting both homolytic and heterolytic BDEs for molecules of any charge. To test the model, we have constructed a dataset of both homolytic and heterolytic BDEs for neutral and charged (1 and +1) molecules. BonDNet achieves a mean absolute error (MAE) of 0.022 eV for unseen test data, significantly below chemical accuracy (0.043 eV). Besides the ability to handle complex bond dissociation reactions that no previous model could con- sider, BonDNet distinguishes itself even in only predicting homolytic BDEs for neutral molecules; it achieves an MAE of 0.020 eV on the PubChem BDE dataset, a 20% improvement over the previous best performing model. We gain additional insight into the model’s predictions by analyzing the patterns in the features representing the molecules and the bond dissociation reactions, which are qualitatively consistent with chemical rules and intuition. BonDNet is just one application of our general approach to representing and learning chemical reactivity, and it could be easily extended to the prediction of other reaction properties in the future.</p></div></div></div>

scholarly journals A Neural Network Based Algorithm for Dynamically Adjusting Activity Targets to Sustain Exercise Engagement Among People Using Activity Trackers

2019 ◽  
Author(s):  
Ramin Mohammadi ◽  
Amanda Jayne Centi ◽  
Mursal Atif ◽  
Stephen Agboola ◽  
Kamal Jethwani ◽  
...  
Keyword(s):  
Machine Learning ◽  
Performance Metrics ◽  
Absolute Error ◽  
Learning Model ◽  
Step Count ◽  
Entropy Measure ◽  
Daily Step ◽  
Activity Trackers ◽  
Machine Learning Model ◽  
Daily Step Count

AbstractIt is well established that lack of physical activity is detrimental to overall health of an individual. Modern day activity trackers enable individuals to monitor their daily activity to meet and maintain targets and to promote activity encouraging behavior. However, the benefits of activity trackers are attenuated over time due to waning adherence. One of the key methods to improve adherence to goals is to motivate individuals to improve on their historic performance metrics. In this work we developed a machine learning model to dynamically adjust the activity target for the forthcoming week that can be realistically achieved by the activity-tracker users. This model prescribes activity target for the forthcoming week. We considered individual user-specific personal, social, and environmental factors, daily step count through the current week (7 days). In addition, we computed an entropy measure that characterizes the pattern of daily step count for the current week. Data for training the machine learning model was collected from 30 participants over a duration of 9 weeks. The model predicted target daily count with mean absolute error of 1545 steps. The proposed work can be used to set personalized goals in accordance with the individual’s level of activity and thereby improving adherence to fitness tracker.

scholarly journals Machine learning model to project the impact of COVID-19 on US motor gasoline demand

Nature Energy ◽  
2020 ◽  
Vol 5 (9) ◽  
pp. 666-673 ◽  
Author(s):  
Shiqi Ou ◽  
Xin He ◽  
Weiqi Ji ◽  
Wei Chen ◽  
Lang Sui ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Model ◽  
Gasoline Demand ◽  
Motor Gasoline ◽  
Machine Learning Model ◽  
The Impact

scholarly journals Gene Expression Profiles in Sporadic ALS Fibroblasts Define Disease Subtypes and The Metabolic Effects of the Investigational Drug EH301

2021 ◽  
Author(s):  
Jasmine A. Fels ◽  
Gabriella Casalena ◽  
Csaba Konrad ◽  
Holly Holmes ◽  
Ryan W. Dellinger ◽  
...  
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Disease Progression ◽  
Metabolic Stress ◽  
Learning Model ◽  
Metabolic Effects ◽  
Investigational Drug ◽  
Machine Learning Model ◽  
Sporadic Als ◽  
The Impact

Abstract Background: Majority of ALS cases are sporadic (sALS), as they lack defined genetic causes. Metabolic alterations shared between the nervous system and skin fibroblasts have emerged in ALS. Recently, we found that a subgroup of sALS fibroblasts (sALS1) is characterized by metabolic profiles (metabotype) distinct from other sALS cases (sALS2) and controls, suggesting that metabolic therapies could be effective in sALS. The metabolic modulators nicotinamide riboside and pterostilbene (EH301) are under clinical development for the treatment of ALS. Here, we studied the metabolome and transcriptome of sALS cells to understand the molecular bases of sALS metabotypes and the impact of EH301.Methods: Six fibroblast cell lines (3 male and 3 female subjects of similar ages) were used for each group (sALS1, sALS2, and controls). Metabolomics and transcriptomics were investigated at baseline and after EH301 treatment. Differential gene expression (DEGs) and metabolite abundance were assessed by a Wald Test and ANOVA, respectively, with FDR correction, and pathway analyses were performed. EH301 protection against metabolic stress was tested by thiol depletion. Weighted gene co-expression network analysis (WGCNA) was used to investigate the association of metabolic and clinical features and was also performed on the Answer ALS dataset from induced motor neurons (iMN). A machine learning model based on DEGs was tested as a sALS disease progression predictor. Results: We found that the sALS1 transcriptome is distinct from sALS2 and that EH301 modifies gene expression differently in sALS1, sALS2, and controls. Furthermore, EH301 had strong protective effects against metabolic stress, which is linked to anti-inflammatory and antioxidant pathways. WGCNA revealed that ALS functional rating scale and metabotypes are associated with gene modules enriched for cell cycle, immunity, autophagy, and metabolism terms, which are modified by EH301. Meta-analysis of publicly available transcriptomics data from iMNs confirmed functional associations of genes correlated with disease traits. A small subset of genes differentially expressed in sALS fibroblasts could be used in a machine learning model to predict disease progression.Conclusions: Multi-omics analyses of patient-derived fibroblasts highlighted differential metabolic and transcriptomic profiles in sALS metabotypes, which translate into differential responses to the investigational drug EH301.

scholarly journals BonDNet: A Graph Neural Network for the Prediction of Bond Dissociation Energies for Charged Molecules

2020 ◽  
Author(s):  
Mingjian Wen ◽  
Samuel Blau ◽  
Evan Spotte-Smith ◽  
Shyam Dwaraknath ◽  
Kristin Persson
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Chemical Reactivity ◽  
Absolute Error ◽  
Learning Model ◽  
Chemical Processes ◽  
Accurate Prediction ◽  
Global Features ◽  
Bond Dissociation ◽  
Machine Learning Model

<div><div><div><p>A broad collection of technologies, including e.g. drug metabolism, biofuel combustion, photochemical decontamination of water, and interfacial passivation in energy production/storage systems rely on chemical processes that involve bond-breaking molecular reactions. In this context, a fundamental thermodynamic property of interest is the bond dissociation energy (BDE) which measures the strength of a chemical bond. Fast and accurate prediction of BDEs for arbitrary molecules would lay the groundwork for data-driven projections of complex reaction cascades and hence a deeper understanding of these critical chemical processes and, ultimately, how to reverse design them. In this paper, we propose a chemically inspired graph neural network machine learning model, BonDNet, for the rapid and accurate prediction of BDEs. BonDNet maps the difference between the molecular representations of the reactants and products to the reaction BDE. Because of the use of this difference representation and the introduction of global features, including molecular charge, it is the first machine learning model capable of predicting both homolytic and heterolytic BDEs for molecules of any charge. To test the model, we have constructed a dataset of both homolytic and heterolytic BDEs for neutral and charged (1 and +1) molecules. BonDNet achieves a mean absolute error (MAE) of 0.022 eV for unseen test data, significantly below chemical accuracy (0.043 eV). Besides the ability to handle complex bond dissociation reactions that no previous model could con- sider, BonDNet distinguishes itself even in only predicting homolytic BDEs for neutral molecules; it achieves an MAE of 0.020 eV on the PubChem BDE dataset, a 20% improvement over the previous best performing model. We gain additional insight into the model’s predictions by analyzing the patterns in the features representing the molecules and the bond dissociation reactions, which are qualitatively consistent with chemical rules and intuition. BonDNet is just one application of our general approach to representing and learning chemical reactivity, and it could be easily extended to the prediction of other reaction properties in the future.</p></div></div></div>

scholarly journals Forecasting Amazon Rain-Forest Deforestation Using a Hybrid Machine Learning Model

2022 ◽  
Vol 14 (2) ◽  
pp. 691
Author(s):  
David Dominguez ◽  
Luis de Juan del Villar ◽  
Odette Pantoja ◽  
Mario González-Rodríguez
Keyword(s):  
Machine Learning ◽  
Regression Model ◽  
Rain Forest ◽  
Time Series Data ◽  
Learning Model ◽  
Series Data ◽  
Amazon Rain Forest ◽  
Machine Learning Model ◽  
Hybrid Machine ◽  
The Impact

The present work aims to carry out an analysis of the Amazon rain-forest deforestation, which can be analyzed from actual data and predicted by means of artificial intelligence algorithms. A hybrid machine learning model was implemented, using a dataset consisting of 760 Brazilian Amazon municipalities, with static data, namely geographical, forest, and watershed, among others, together with a time series data of annual deforestation area for the last 20 years (1999–2019). The designed learning model combines dense neural networks for the static variables and a recurrent Long Short Term Memory neural network for the temporal data. Many iterations were performed on augmented data, testing different configurations of the regression model, for adjusting the model hyper-parameters, and generating a battery of tests to obtain the optimal model, achieving a R-squared score of 87.82%. The final regression model predicts the increase in annual deforestation area (square kilometers), for a decade, from 2020 to 2030, predicting that deforestation will reach 1 million square kilometers by 2030, accounting for around 15% compared with the present 1%, of the between 5.5 and 6.7 millions of square kilometers of the rain-forest. The obtained results will help to understand the impact of man’s footprint on the Amazon rain-forest.

scholarly journals Multitask machine learning models for predicting lipophilicity (logP) in the SAMPL7 challenge

2021 ◽  
Author(s):  
Eelke B. Lenselink ◽  
Pieter F. W. Stouten
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Drug Discovery ◽  
Message Passing ◽  
Learning Model ◽  
Molecular Structures ◽  
Learning Models ◽  
Final Model ◽  
Machine Learning Model ◽  
Machine Learning Models

AbstractAccurate prediction of lipophilicity—logP—based on molecular structures is a well-established field. Predictions of logP are often used to drive forward drug discovery projects. Driven by the SAMPL7 challenge, in this manuscript we describe the steps that were taken to construct a novel machine learning model that can predict and generalize well. This model is based on the recently described Directed-Message Passing Neural Networks (D-MPNNs). Further enhancements included: both the inclusion of additional datasets from ChEMBL (RMSE improvement of 0.03), and the addition of helper tasks (RMSE improvement of 0.04). To the best of our knowledge, the concept of adding predictions from other models (Simulations Plus logP and [email protected], respectively) as helper tasks is novel and could be applied in a broader context. The final model that we constructed and used to participate in the challenge ranked 2/17 ranked submissions with an RMSE of 0.66, and an MAE of 0.48 (submission: Chemprop). On other datasets the model also works well, especially retrospectively applied to the SAMPL6 challenge where it would have ranked number one out of all submissions (RMSE of 0.35). Despite the fact that our model works well, we conclude with suggestions that are expected to improve the model even further.

scholarly journals BonDNet: A Graph Neural Network for the Prediction of Bond Dissociation Energies for Charged Molecules

2020 ◽  
Author(s):  
Mingjian Wen ◽  
Samuel Blau ◽  
Evan Spotte-Smith ◽  
Shyam Dwaraknath ◽  
Kristin Persson
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Chemical Reactivity ◽  
Absolute Error ◽  
Learning Model ◽  
Chemical Processes ◽  
Accurate Prediction ◽  
Global Features ◽  
Bond Dissociation ◽  
Machine Learning Model

<div><div><div><p>A broad collection of technologies, including e.g. drug metabolism, biofuel combustion, photochemical decontamination of water, and interfacial passivation in energy production/storage systems rely on chemical processes that involve bond-breaking molecular reactions. In this context, a fundamental thermodynamic property of interest is the bond dissociation energy (BDE) which measures the strength of a chemical bond. Fast and accurate prediction of BDEs for arbitrary molecules would lay the groundwork for data-driven projections of complex reaction cascades and hence a deeper understanding of these critical chemical processes and, ultimately, how to reverse design them. In this paper, we propose a chemically inspired graph neural network machine learning model, BonDNet, for the rapid and accurate prediction of BDEs. BonDNet maps the difference between the molecular representations of the reactants and products to the reaction BDE. Because of the use of this difference representation and the introduction of global features, including molecular charge, it is the first machine learning model capable of predicting both homolytic and heterolytic BDEs for molecules of any charge. To test the model, we have constructed a dataset of both homolytic and heterolytic BDEs for neutral and charged (1 and +1) molecules. BonDNet achieves a mean absolute error (MAE) of 0.022 eV for unseen test data, significantly below chemical accuracy (0.043 eV). Besides the ability to handle complex bond dissociation reactions that no previous model could con- sider, BonDNet distinguishes itself even in only predicting homolytic BDEs for neutral molecules; it achieves an MAE of 0.020 eV on the PubChem BDE dataset, a 20% improvement over the previous best performing model. We gain additional insight into the model’s predictions by analyzing the patterns in the features representing the molecules and the bond dissociation reactions, which are qualitatively consistent with chemical rules and intuition. BonDNet is just one application of our general approach to representing and learning chemical reactivity, and it could be easily extended to the prediction of other reaction properties in the future.</p></div></div></div>

scholarly journals Exploring the Impact of Data Poisoning Attacks on Machine Learning Model Reliability

2021 ◽  
Vol 192 ◽  
pp. 2624-2632
Author(s):  
Laura Verde ◽  
Fiammetta Marulli ◽  
Stefano Marrone
Keyword(s):  
Machine Learning ◽  
Learning Model ◽  
Machine Learning Model ◽  
Model Reliability ◽  
The Impact
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