Predicting Compound Melting Temperature from Computationally Derived Properties via Machine Learning
DOI:
https://doi.org/10.47611/jsrhs.v13i3.7279Keywords:
melting temperature, melting point, melting temperature prediction, machine learning, random forest, regression, properties, data augmentation, compound groups, elastic moduli, bulk modulus, shear modulus, electronegativity, energy, atomic rowAbstract
Melting temperature is a fundamental material property used in a wide variety of scientific disciplines. To eliminate the need for experimentally measuring melting temperature, often costly and potentially dangerous, we developed a new computational approach that predicts a compound’s melting temperature from its material properties, computed by Density-functional theory or other computational methods, using machine learning models. The proposed machine learning models achieved promising results, with a Mean Percentage Absolute Error of 15%-20%. We also identified important material properties in predicting melting temperature across various groups of compounds containing metals, transition metals, post transition metals, alkaline earth metals, halogens, and chalcogens.
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