scholarly journals Simulating the Ghost: Quantum Dynamics of the Solvated Electron

2020 ◽  
Author(s):  
Jinggang Lan ◽  
Venkat Kapil ◽  
Piero Gasparotto ◽  
Michele Ceriotti ◽  
Marcella Iannuzzi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quantum Dynamics ◽  
Predictive Accuracy ◽  
Accurate Determination ◽  
Learning Model ◽  
Structure Theory ◽  
Solvated Electron ◽  
Surrounding Water ◽  
Machine Learning Model ◽  
Cavity Structure

The nature of bulk hydrated electron has been a challenge for both experiment and theory due to its short life time and high reactivity, and the need for a high-level of electronic structure theory to achieve predictive accuracy. The lack of a classical atomistic structural formula makes it exceedingly difficult to model the solvated electron using conventional empirical force fields, which describe the system in terms of interactions between point particles associated with atomic nuclei. Here we overcome this problem using a machine-learning model, that is sufficiently flexible to describe the effect of the excess electron on the structure of the surrounding water, without including the electron in the model explicitly. The resulting potential is not only able to reproduce the stable cavity structure, but also recovers the correct localization dynamics that follows the injection of an electron in neat water. The machine learning model achieves the accuracy of the state-of-the-art correlated wave function method it is trained on. It is sufficiently inexpensive to afford a full quantum statistical and dynamical description, and allows us to achieve a highly accurate determination of the structure, diffusion mechanisms and vibrational spectroscopy of the solvated electron

scholarly journals Simulating the Ghost: Quantum Dynamics of the Solvated Electron

2020 ◽  
Author(s):  
Jinggang Lan ◽  
Venkat Kapil ◽  
Piero Gasparotto ◽  
Michele Ceriotti ◽  
Marcella Iannuzzi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quantum Dynamics ◽  
Predictive Accuracy ◽  
Accurate Determination ◽  
Learning Model ◽  
Structure Theory ◽  
Solvated Electron ◽  
Surrounding Water ◽  
Machine Learning Model ◽  
Cavity Structure

The nature of bulk hydrated electron has been a challenge for both experiment and theory due to its short life time and high reactivity, and the need for a high-level of electronic structure theory to achieve predictive accuracy. The lack of a classical atomistic structural formula makes it exceedingly difficult to model the solvated electron using conventional empirical force fields, which describe the system in terms of interactions between point particles associated with atomic nuclei. Here we overcome this problem using a machine-learning model, that is sufficiently flexible to describe the effect of the excess electron on the structure of the surrounding water, without including the electron in the model explicitly. The resulting potential is not only able to reproduce the stable cavity structure, but also recovers the correct localization dynamics that follows the injection of an electron in neat water. The machine learning model achieves the accuracy of the state-of-the-art correlated wave function method it is trained on. It is sufficiently inexpensive to afford a full quantum statistical and dynamical description, and allows us to achieve a highly accurate determination of the structure, diffusion mechanisms and vibrational spectroscopy of the solvated electron

scholarly journals Simulating the ghost: quantum dynamics of the solvated electron

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Jinggang Lan ◽  
Venkat Kapil ◽  
Piero Gasparotto ◽  
Michele Ceriotti ◽  
Marcella Iannuzzi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quantum Dynamics ◽  
Predictive Accuracy ◽  
Accurate Determination ◽  
Learning Model ◽  
Structure Theory ◽  
Solvated Electron ◽  
Surrounding Water ◽  
Machine Learning Model ◽  
Cavity Structure

AbstractThe nature of the bulk hydrated electron has been a challenge for both experiment and theory due to its short lifetime and high reactivity, and the need for a high-level of electronic structure theory to achieve predictive accuracy. The lack of a classical atomistic structural formula makes it exceedingly difficult to model the solvated electron using conventional empirical force fields, which describe the system in terms of interactions between point particles associated with atomic nuclei. Here we overcome this problem using a machine-learning model, that is sufficiently flexible to describe the effect of the excess electron on the structure of the surrounding water, without including the electron in the model explicitly. The resulting potential is not only able to reproduce the stable cavity structure but also recovers the correct localization dynamics that follow the injection of an electron in neat water. The machine learning model achieves the accuracy of the state-of-the-art correlated wave function method it is trained on. It is sufficiently inexpensive to afford a full quantum statistical and dynamical description and allows us to achieve accurate determination of the structure, diffusion mechanisms, and vibrational spectroscopy of the solvated electron.

scholarly journals GP.6 Improving Triaging of EEG Referrals for Rule out Infantile Spasms (ITERIS)

2021 ◽  
Vol 48 (s3) ◽  
pp. S13-S13
Author(s):  
D Djordjevic ◽  
J Tracey ◽  
M Alqahtani ◽  
J Boyd ◽  
C Go
Keyword(s):  
Machine Learning ◽  
Predictive Factors ◽  
Predictive Accuracy ◽  
Learning Model ◽  
Infantile Spasms ◽  
Low Risk ◽  
Machine Learning Techniques ◽  
Risk Category ◽  
Point System ◽  
Machine Learning Model

Background: Infantile spasms (IS) is a devastating pediatric seizure disorder for which EEG referrals are prioritized at the Hospital for Sick Children, representing a resource challenge. The goal of this study was to improve the triaging system for these referrals. Methods: Part 1: descriptive analysis was performed retrospectively on EEG referrals. Part 2: prospective questionnaires were used to determine relative risk of various predictive factors. Part 3: electronic referral form was amended to include 5 positive predictive factors. A triage point system was tested by assigning EEGs as high risk (3 days), standard risk (1 week), or low risk (2 weeks). A machine learning model was developed. Results: Most EEG referrals were from community pediatricians with a low yield of IS diagnoses. Using the 5 predictive factors, the proposed triage system accurately diagnosed all IS within 3 days. No abnormal EEGs were missed in the low-risk category. The machine learning model had over 90% predictive accuracy and will be prospectively tested. Conclusions: Improving EEG triaging for IS may be possible to prioritize higher risk patients. Machine Learning techniques can potentially be applied to help with predictions. We hope that our findings will ultimately improve resource utilization and patient care.

scholarly journals Technical note: how to rationally compare the performances of different machine learning models?

2018 ◽  
Author(s):  
Terazima Maeda
Keyword(s):  
Machine Learning ◽  
Predictive Accuracy ◽  
Learning Model ◽  
Technical Note ◽  
Learning Models ◽  
Specific Data ◽  
Machine Learning Model ◽  
Specific Prediction ◽  
Data Group ◽  
Machine Learning Models

Nowadays, there is a large number of machine learning models that could be used for various areas. However, different research targets are usually sensitive to the type of models. For a specific prediction target, the predictive accuracy of a machine learning model is always dependent to the data feature, data size and the intrinsic relationship between inputs and outputs. Therefore, for a specific data group and a fixed prediction mission, how to rationally compare the predictive accuracy of different machine learning model is a big question. In this brief note, we show how should we compare the performances of different machine models by raising some typical examples.

scholarly journals Technical note: how to rationally compare the performances of different machine learning models?

2018 ◽  
Author(s):  
Terazima Maeda
Keyword(s):  
Machine Learning ◽  
Predictive Accuracy ◽  
Learning Model ◽  
Technical Note ◽  
Learning Models ◽  
Specific Data ◽  
Machine Learning Model ◽  
Specific Prediction ◽  
Data Group ◽  
Machine Learning Models

Nowadays, there is a large number of machine learning models that could be used for various areas. However, different research targets are usually sensitive to the type of models. For a specific prediction target, the predictive accuracy of a machine learning model is always dependent to the data feature, data size and the intrinsic relationship between inputs and outputs. Therefore, for a specific data group and a fixed prediction mission, how to rationally compare the predictive accuracy of different machine learning model is a big question. In this brief note, we show how should we compare the performances of different machine models by raising some typical examples.

Machine Learning Accelerated Genetic Algorithms for Computational Materials Search

2018 ◽  
Author(s):  
Steen Lysgaard ◽  
Paul C. Jennings ◽  
Jens Strabo Hummelshøj ◽  
Thomas Bligaard ◽  
Tejs Vegge
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Au Nanoparticles ◽  
Learning Model ◽  
Energy Calculations ◽  
Atomic Distribution ◽  
Machine Learning Model ◽  
Fold Reduction ◽  
Computational Materials

A machine learning model is used as a surrogate fitness evaluator in a genetic algorithm (GA) optimization of the atomic distribution of Pt-Au nanoparticles. The machine learning accelerated genetic algorithm (MLaGA) yields a 50-fold reduction of required energy calculations compared to a traditional GA.

AN EFFICIENT MACHINE LEARNING MODEL FOR PREDICTION OF ACUTE MYOCARDIAL INFARCTION

2020 ◽  
Vol 13 ◽  
Author(s):  
Dhilsath Fathima.M ◽  
S. Justin Samuel ◽  
R. Hari Haran
Keyword(s):  
Machine Learning ◽  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Logistic Regression ◽  
Decision Tree ◽  
Learning Model ◽  
Training Dataset ◽  
Data Set ◽  
Machine Learning Model ◽  
Proposed Model

Aim: This proposed work is used to develop an improved and robust machine learning model for predicting Myocardial Infarction (MI) could have substantial clinical impact. Objectives: This paper explains how to build machine learning based computer-aided analysis system for an early and accurate prediction of Myocardial Infarction (MI) which utilizes framingham heart study dataset for validation and evaluation. This proposed computer-aided analysis model will support medical professionals to predict myocardial infarction proficiently. Methods: The proposed model utilize the mean imputation to remove the missing values from the data set, then applied principal component analysis to extract the optimal features from the data set to enhance the performance of the classifiers. After PCA, the reduced features are partitioned into training dataset and testing dataset where 70% of the training dataset are given as an input to the four well-liked classifiers as support vector machine, k-nearest neighbor, logistic regression and decision tree to train the classifiers and 30% of test dataset is used to evaluate an output of machine learning model using performance metrics as confusion matrix, classifier accuracy, precision, sensitivity, F1-score, AUC-ROC curve. Results: Output of the classifiers are evaluated using performance measures and we observed that logistic regression provides high accuracy than K-NN, SVM, decision tree classifiers and PCA performs sound as a good feature extraction method to enhance the performance of proposed model. From these analyses, we conclude that logistic regression having good mean accuracy level and standard deviation accuracy compared with the other three algorithms. AUC-ROC curve of the proposed classifiers is analyzed from the output figure.4, figure.5 that logistic regression exhibits good AUC-ROC score, i.e. around 70% compared to k-NN and decision tree algorithm. Conclusion: From the result analysis, we infer that this proposed machine learning model will act as an optimal decision making system to predict the acute myocardial infarction at an early stage than an existing machine learning based prediction models and it is capable to predict the presence of an acute myocardial Infarction with human using the heart disease risk factors, in order to decide when to start lifestyle modification and medical treatment to prevent the heart disease.

Smart-ML: A System for Machine Learning Model Exploration using Pipeline Graph

2020 ◽  
Author(s):  
Dhaval Patel ◽  
Shrey Shrivastava ◽  
Wesley Gifford ◽  
Stuart Siegel ◽  
Jayant Kalagnanam ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Model ◽  
Machine Learning Model
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