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Formation enthalpies for transition metal alloys using machine learning
Shashanka Ubaru
, Agnieszka Miȩdlar
,
Yousef Saad
, James R. Chelikowsky
Computer Science and Engineering
Research output
:
Contribution to journal
›
Article
›
peer-review
30
Scopus citations
Overview
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Keyphrases
Machine Learning
100%
Enthalpy of Formation
100%
Transition Metal Alloys
100%
Thermodynamic Properties
33%
Machine Learning System
16%
Materials Informatics
16%
Sensitivity Analysis
16%
Semi-empirical Model
16%
Predictive Ability
16%
Metal Alloys
16%
Feature-based
16%
Accurate Method
16%
Least Absolute Shrinkage and Selection Operator (LASSO)
16%
Feature Learning
16%
Feature Selection Methods
16%
Binary Alloy
16%
Least Squares Support Vector Regression (LSSVR)
16%
Fast Method
16%
Intermetallic Compounds
16%
Material Data
16%
Machine Learning Based
16%
Binary Intermetallics
16%
Nonlinear Kernels
16%
Miedema Model
16%
Popular
16%
Kernel-based
16%
Operator Basis
16%
Physics Knowledge
16%
Material Science
Transition Metal
100%
Binary Alloy
100%
Transition Metal Alloys
100%
Intermetallics
100%
Chemical Engineering
Learning System
100%
Enthalpy
100%
Transition Metal
100%
Support Vector Machine
16%
Feature Extraction
16%
Engineering
Learning System
100%
Feature Extraction
25%
Least Absolute Shrinkage and Selection Operator
25%
Input Feature
25%
Predictive Ability
25%
Material Data
25%
Selection Method
25%
Binary Alloy
25%
Support Vector Machine
25%
Intermetallics
25%
Chemistry
Enthalpy of Formation
100%
Transition Metal Alloys
100%
Intermetallic Compound
14%
Transition Metal
14%