scholarly journals Feature optimization for atomistic machine learning yields a data-driven construction of the periodic table of the elements

2018 ◽  
Vol 20 (47) ◽  
pp. 29661-29668 ◽  
Author(s):  
Michael J. Willatt ◽  
Félix Musil ◽  
Michele Ceriotti
Keyword(s):  
Machine Learning ◽  
Periodic Table ◽  
Chemical Space ◽  
Learning Model ◽  
Data Driven ◽  
Machine Learning Model ◽  
Feature Optimization ◽  
Low Dimensional

By representing elements as points in a low-dimensional chemical space it is possible to improve the performance of a machine-learning model for a chemically-diverse dataset. The resulting coordinates are reminiscent of the main groups of the periodic table.

A Data-Driven Framework for Buckling Analysis of Near-Spherical Composite Shells under External Pressure

2021 ◽  
pp. 1-27
Author(s):  
Mitansh Doshi ◽  
Xin Ning
Keyword(s):  
Machine Learning ◽  
Finite Element ◽  
Learning Model ◽  
External Pressure ◽  
Finite Element Simulations ◽  
Data Driven ◽  
Composite Shells ◽  
Spherical Shells ◽  
Buckling Loads ◽  
Machine Learning Model

Abstract This paper presents a data-driven framework that can accurately predict the buckling loads of composite near-spherical shells (i.e. variants of regular icosahedral shells) under external pressure. This framework utilizes finite element simulations to generate data to train a machine learning regression model based on open-source algorithm Extreme Gradient Boosting (XGBoost). The trained XGBoost machine learning model can then predict buckling loads of new designs with small margin of error without time-consuming finite element simulations. Examples of near-spherical composite shells with various geometries and material layups demonstrate the efficiency and accuracy of the framework. The machine learning model removes the demanding hardware and software requirements on computing buckling loads of near-spherical shells, making it particularly suitable to users without access to those computational resources.

scholarly journals CarEnvision: A Data-Driven Machine Learning Framework for Automated Car Value Prediction

2021 ◽  
Author(s):  
TianGe (Terence) Chen ◽  
Angel Chang ◽  
Evan Gunnell ◽  
Yu Sun
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Learning Model ◽  
Retail Price ◽  
Data Driven ◽  
Value Prediction ◽  
Learning Framework ◽  
Machine Learning Model ◽  
Negative Sentiment

When people want to buy or sell a personal car, they struggle to know when the timing is best in order to buy their favorite vehicle for the best price or sell for the most profit. We have come up with a program that can predict each car’s future values based on experts’ opinions and reviews. Our program extracts reviews which undergo sentiment analysis to become our data in the form of positive and negative sentiment. The data is then collected and used to train the Machine Learning model, which will in turn predict the car’s retail price.

Molecular Transformer unifies reaction prediction and retrosynthesis across pharma chemical space

2019 ◽  
Vol 55 (81) ◽  
pp. 12152-12155 ◽  
Author(s):  
Alpha A. Lee ◽  
Qingyi Yang ◽  
Vishnu Sresht ◽  
Peter Bolgar ◽  
Xinjun Hou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chemical Space ◽  
Learning Model ◽  
Reaction Prediction ◽  
Machine Learning Model

We develop a machine learning model that tackles both reaction prediction and retrosynthesis by learning from the same dataset. The model is generalizable across chemical space.

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.

Sign in / Sign up

Export Citation Format

Share Document