scholarly journals Improved Prediction and Understanding of Glass-Forming Ability Based on Random Forest Algorithm

2021 ◽  
Vol 3 (2) ◽  
pp. 79-87
Author(s):  
Chenjing Su ◽  
Xiaoyu Li ◽  
Mengru Li ◽  
Qinsheng Zhu ◽  
Hao Fu ◽  
...  
Keyword(s):  
Random Forest ◽  
Glass Forming Ability ◽  
Random Forest Algorithm ◽  
Glass Forming

scholarly journals Prediction of Glass Forming Ability of Bulk Metallic Glasses-Machine-Learning

2021 ◽  
Author(s):  
Jaideep Reddy Gedi ◽  
Tanay Saboo ◽  
KAMESWARI PRASADA Rao AYYAGARI
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Critical Thickness ◽  
Glassy Alloy ◽  
Glass Forming Ability ◽  
Machine Learning Techniques ◽  
Glass Forming ◽  
Artificial Neural

Abstract Bulk-Metallic-Glass has been a fascinating class of metallic systems with remarkable corrosion resistance, elastic modulus and wear resistance, while evaluating the glass forming ability has been a very interesting aspect for decades. Machine learning techniques viz., artificial neural networks and random-forest based models have been developed in this work to predict the glass forming ability, given the composition of the bulk metallic glassy alloy. A new criterion of classification of atoms present in a bulk metallic glassy alloy is proposed. Feature importance analysis confirmed that the accuracy of the prediction depends mainly on change in enthalpy of mixing and change in entropy of mixing. However, among the artificial neural network random forest models developed, the former showed a promising accuracy in prediction of the glass formation ability (critical thickness). It has been successfully demonstrated and validated with experimental critical thickness that the glass forming ability can be predicted using an artificial neural network given the elemental composition alone. A computational algorithm was also developed to classify the atoms as big/ small in a given alloy. The outcome of this algorithm was used by models developed by training with experimental data.

METHOD SUGGESTING CITY WALKING ROUTES FOR PEDESTRIANS USING AN EXAMPLE OF SAINT-PETERSBURG

2019 ◽  
pp. 71-76
Author(s):  
A.E. Semenov
Keyword(s):  
Random Forest ◽  
Develop Model ◽  
Random Forest Algorithm ◽  
Saint Petersburg ◽  
Pedestrian Navigation ◽  
Factors Influencing ◽  
The City

The method of pedestrian navigation in the cities illustrated by the example of Saint-Petersburg was investigated. The factors influencing people when they choose a route for their walk were determined. Based on acquired factors corresponding data was collected and used to develop model determining attractiveness of a street in the city using Random Forest algorithm. The results obtained shows that routes provided by the method are 14% more attractive and just 6% longer compared with the shortest ones.

Induction period of the crystallization of silver nitrate solutions in ethylene glycol

1991 ◽  
Vol 56 (10) ◽  
pp. 2142-2147
Author(s):  
Ivo Sláma
Keyword(s):  
Ethylene Glycol ◽  
Silver Nitrate ◽  
Induction Period ◽  
Glass Forming Ability ◽  
Transformation Theory ◽  
Concentration Region ◽  
Glass Forming ◽  
Temperature Transformation ◽  
Silver Nitrate Solutions ◽  
Nitrate Solutions

The dependence of the induction period of crystallization on supercooling was examined for the silver nitrate-ethylene glycol system over the concentration region of silver nitrate lome fraction of 0 to 0.12. Addition of AgNO3 to ethylene glycol was found to increase considerably the critical induction period of crystallization, although to a lesser extent than Ca(NO3)2, CaCl2, ZnCl2, LiCl and LiNO3 do. The effect of these salts on the critical induction period of crystallization in dimethylsulfoxide, dimethylformamide, dimethylacetamide and methanol was compared in terms of the solvent-rich composition limit of the glass-forming ability. By using the TTT(Time-Temperature-Transformation) theory, it has been deduced that the effect of the salts on the critical induction period of crystallization of ethylene glycol is probably due to the different dependences of viscosity on their concentration in ethylene glyco in the supercooling region.

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