Jiafan Cao, G Bhuvaneswari, Thangaraja Arumugam, B R Aravind
The digital edge: examining the relationship between digital competency and language learning outcomes
Frontiers in psychology
accurate prediction standard vaporization enthalpy vaphm volatile organic compounds vocs paramount importance environmental chemistry industrial applications regulatory compliance overcome traditional experimental methods predicting vaphm vocs machine learning ml models enable highthroughput costeffective property estimation despite rising momentum existing ml algorithms still present limitations prediction accuracy broad chemical applications work present data driven explainable supervised ml model predict vaphm vocs model built established experimental database 2410 unique molecules 223 vocs categorized chemical groups using supervised ml regression algorithms random forest successfully predicted vocs vaphm mean absolute error 302 kj mol1 95 test score model successfully validated prediction vaphm known database vocs molecular group holdout tests chemical feature importance analysis explainable model revealed voc polarizability connectivity indexes electrotopological state key models prediction accuracy thus present replicable explainable model expanded towards prediction thermodynamic properties vocs