首页|State Key Laboratory Reports Findings in Machine Learning (Pre- dicting the enthalpy of formation of energetic molecules via con- ventional machine learning and GNN)
State Key Laboratory Reports Findings in Machine Learning (Pre- dicting the enthalpy of formation of energetic molecules via con- ventional machine learning and GNN)
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NETL
NSTL
Royal Soc Chemistry
New research on Machine Learning is the subject of a report. According to news originating from Beijing, People's Republic of China, by NewsRx correspondents, research stated, "Machine learning (ML) provides a promising method for efficiently and accurately predicting molecular properties. Using ML models to predict the enthalpy of formation of energetic molecules helps in fast screening of potential high-energy molecules, thereby accelerating the design of energetic materials." Financial support for this research came from State Key Laboratory of Explosion Science and Technol- ogy. Our news journalists obtained a quote from the research from State Key Laboratory, "A persistent challenge is to determine the optimal featurization methods for molecular representation and use an ap- propriate ML model. Thus, in our study, we evaluate various featurization methods (CDS, ECFP, SOAP, GNF) and ML models (RF, MLP, GCN, MPNN), dividing them into two groups: conventional ML models and GNN models, to predict the enthalpy of formation of potential high-energy molecules. Our results demonstrate that CDS and SOAP have advantages over the ECFP, while the GNFs in GCN and MPNN models perform better. Furthermore, the MPNN model performs best among all models with a root mean square error (RMSE) as low as 8.42 kcal mol, surpassing even the best performing CDS-MLP model in conventional ML models."
BeijingPeople's Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine Learning
NETL
NSTL
Royal Soc Chemistry
