Machine Learning for Predicting the Critical Yield Stress of High Entropy Alloys

2020 ◽  
Vol 143 (2) ◽  
Author(s):  
Pau Cutrina Vilalta ◽  
Somayyeh Sheikholeslami ◽  
Katerine Saleme Ruiz ◽  
Xin C. Yee ◽  
Marisol Koslowski
Keyword(s):  
Machine Learning ◽  
Yield Stress ◽  
Phase Field ◽  
Stacking Fault ◽  
Dislocation Dynamics ◽  
High Entropy Alloys ◽  
Learning Models ◽  
High Entropy ◽  
Critical Yield ◽  
Machine Learning Models

Abstract We applied machine learning models to predict the relationship between the yield stress and the stacking fault energies landscape in high entropy alloys. The data for learning in this work were taken from phase-field dislocation dynamics simulations of partial dislocations in face-centered-cubic metals. This study was motivated by the intensive computation required for phase-field simulations. We adopted three different ways to describe the variations of the stacking fault energy (SFE) landscape as inputs to the machine learning models. Our study showed that the best machine learning model was able to predict the yield stress to approximately 2% error. In addition, our unsupervised learning study produced a principal component that showed the same trend as a physically meaningful quantity with respect to the critical yield stress.

Ensemble-based machine learning models for phase prediction in high entropy alloys

2021 ◽  
pp. 111025
Author(s):  
Aayesha Mishra ◽  
Lakshminarayana Kompella ◽  
Lalit Mohan Sanagavarapu ◽  
Sreedevi Varam
Keyword(s):  
Machine Learning ◽  
High Entropy Alloys ◽  
Learning Models ◽  
High Entropy ◽  
Phase Prediction ◽  
Machine Learning Models

Phase prediction in high entropy alloys with a rational selection of materials descriptors and machine learning models

2020 ◽  
Vol 185 ◽  
pp. 528-539 ◽  
Author(s):  
Yan Zhang ◽  
Cheng Wen ◽  
Changxin Wang ◽  
Stoichko Antonov ◽  
Dezhen Xue ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Entropy Alloys ◽  
Learning Models ◽  
Rational Selection ◽  
High Entropy ◽  
Phase Prediction ◽  
Machine Learning Models ◽  
Selection Of

Improving XGBoost with Imagination Sampling

2020 ◽  
Vol 2 (1) ◽  
pp. 3-6
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
General System ◽  
Learning Models ◽  
Starting Point ◽  
Machine Learning Models

Imagination Sampling is the usage of a person as an oracle for generating or improving machine learning models. Previous work demonstrated a general system for using Imagination Sampling for obtaining multibox models. Here, the possibility of importing such models as the starting point for further automatic enhancement is explored.

Epigenetic Target Prediction with Accurate Machine Learning Models

2021 ◽  
Author(s):  
Norberto Sánchez-Cruz ◽  
Jose L. Medina-Franco
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
Predictive Models ◽  
Large Scale ◽  
Target Prediction ◽  
Quantitative Measure ◽  
Learning Models ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Machine Learning Models

<p>Epigenetic targets are a significant focus for drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents a large amount of structure-activity relationships that has not been exploited thus far for the development of predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. Through a systematic comparison of machine learning models trained on molecular fingerprints of different design, we built predictive models with high accuracy for the epigenetic target profiling of small molecules. The models were thoroughly validated showing mean precisions up to 0.952 for the epigenetic target prediction task. Our results indicate that the herein reported models have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as freely accessible and easy-to-use web application.</p>

Utilizing Blockchain Technology in Social Media Bot Identification

2020 ◽  
Author(s):  
Shreya Reddy ◽  
Lisa Ewen ◽  
Pankti Patel ◽  
Prerak Patel ◽  
Ankit Kundal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Gold Standard ◽  
The Internet ◽  
Learning Models ◽  
Current Time ◽  
Machine Learning Methods ◽  
Blockchain Technology ◽  
Modern Age ◽  
Machine Learning Models

<p>As bots become more prevalent and smarter in the modern age of the internet, it becomes ever more important that they be identified and removed. Recent research has dictated that machine learning methods are accurate and the gold standard of bot identification on social media. Unfortunately, machine learning models do not come without their negative aspects such as lengthy training times, difficult feature selection, and overwhelming pre-processing tasks. To overcome these difficulties, we are proposing a blockchain framework for bot identification. At the current time, it is unknown how this method will perform, but it serves to prove the existence of an overwhelming gap of research under this area.<i></i></p>

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