scholarly journals High-throughput and data-mining approach to predict new rare-earth free permanent magnets

2020 ◽  
Vol 101 (9) ◽  
Author(s):  
Alena Vishina ◽  
Olga Yu. Vekilova ◽  
Torbjörn Björkman ◽  
Anders Bergman ◽  
Heike C. Herper ◽  
...  
Keyword(s):  
Data Mining ◽  
Rare Earth ◽  
High Throughput ◽  
Permanent Magnets ◽  
Data Mining Approach

scholarly journals On-the-fly machine-learning for high-throughput experiments: search for rare-earth-free permanent magnets

2014 ◽  
Vol 4 (1) ◽  
Author(s):  
Aaron Gilad Kusne ◽  
Tieren Gao ◽  
Apurva Mehta ◽  
Liqin Ke ◽  
Manh Cuong Nguyen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Rare Earth ◽  
High Throughput ◽  
Permanent Magnets ◽  
High Throughput Experiments

Accelerated Discovery of High-Refractive-Index Polyimides via First-Principles Molecular Modeling, Virtual High-Throughput Screening, and Data Mining

2019 ◽  
Author(s):  
Mohammad Atif Faiz Afzal ◽  
Mojtaba Haghighatlari ◽  
Sai Prasad Ganesh ◽  
Chong Cheng ◽  
Johannes Hachmann
Keyword(s):  
Data Mining ◽  
Refractive Index ◽  
High Throughput ◽  
First Principles ◽  
High Throughput Screening ◽  
Large Scale ◽  
Computational Study ◽  
High Refractive Index ◽  
Structural Features ◽  
Learning Program

<div>We present a high-throughput computational study to identify novel polyimides (PIs) with exceptional refractive index (RI) values for use as optic or optoelectronic materials. Our study utilizes an RI prediction protocol based on a combination of first-principles and data modeling developed in previous work, which we employ on a large-scale PI candidate library generated with the ChemLG code. We deploy the virtual screening software ChemHTPS to automate the assessment of this extensive pool of PI structures in order to determine the performance potential of each candidate. This rapid and efficient approach yields a number of highly promising leads compounds. Using the data mining and machine learning program package ChemML, we analyze the top candidates with respect to prevalent structural features and feature combinations that distinguish them from less promising ones. In particular, we explore the utility of various strategies that introduce highly polarizable moieties into the PI backbone to increase its RI yield. The derived insights provide a foundation for rational and targeted design that goes beyond traditional trial-and-error searches.</div>

Data mining approach to construction productivity prediction for cutter suction dredgers

2019 ◽  
Vol 105 ◽  
pp. 102833 ◽  
Author(s):  
Shuo Bai ◽  
Mingchao Li ◽  
Rui Kong ◽  
Shuai Han ◽  
Heng Li ◽  
...  
Keyword(s):  
Data Mining ◽  
Construction Productivity ◽  
Data Mining Approach ◽  
Productivity Prediction

L10 rare-earth-free permanent magnets: The effects of twinning versus dislocations in Mn-Al magnets

2020 ◽  
Vol 4 (9) ◽  
Author(s):  
Yuxiao Jia ◽  
Yuye Wu ◽  
Shuang Zhao ◽  
Shulan Zuo ◽  
Konstantin P. Skokov ◽  
...  
Keyword(s):  
Rare Earth ◽  
Permanent Magnets
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