首页|China University of Mining and Technology Reports Findings in Machine Learning (General Model for Predicting Response of Gas-Sensitive Materials to Target Gas B ased on Machine Learning)
China University of Mining and Technology Reports Findings in Machine Learning (General Model for Predicting Response of Gas-Sensitive Materials to Target Gas B ased on Machine Learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News - New research on Machine Learning is th e subject of a report. According to newsreporting originating in Jiangsu, Peopl e’s Republic of China, by NewsRx journalists, research stated, “Gassensors play a crucial role in various industries and applications. In recent years, there h as been anincreasing demand for gas sensors in society.”The news reporters obtained a quote from the research from the China University of Mining andTechnology, “However, the current method for screening gas-sensiti ve materials is time-, energy-, andcost-consuming. Consequently, an imperative exists to enhance the screening efficiency. In this study,we proposed a collabo rative screening strategy through integration of density functional theory and machine learning. Taking zinc oxide (ZnO) as an example, the responsiveness of Zn O to the target gas wasdetermined quickly on the basis of the changes in the el ectronic state and structure before and after gasadsorption. In this work, the adsorption energy and electronic and structural characteristics of ZnO afterads orbing 24 kinds of gases were calculated. These computed features served as the basis for training amachine learning model. Subsequently, various machine learn ing and evaluation algorithms were utilizedto train the fast screening model. T he importance of feature values was evaluated by the AdaBoost,Random Forest, an d Extra Trees models. Specifically, charge transfer was assigned importance valu esof 0.160, 0.127, and 0.122, respectively, ranking as the highest among the 11 features. Following closelywas the d-band center, which was presumed to exert influence on electrical conductivity and, consequently,adsorption properties. W ith 5-fold cross-validation using the Extra Tree accuracy, the 24-sample data set achieved an accuracy of 88%. The 72-sample data set achieved an a ccuracy of 78% using multilayerperceptron after 5-fold cross-vali dation, with both data sets exhibiting low standard deviations.”
JiangsuPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine Learning
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