Combining first principles and machine learning for rapid assessment response of WO3 based gas sensors

Abstract

The rapid advancement of gas sensitive properties in metal oxides is crucial for detecting hazardous gases in industrial and coal mining environments. However, the conventional experimental trial and error approach poses significant challenges and resource consumption for the high throughput screening of gas sensitive materials. Consequently, this paper introduced a novel screening approach that integrates first principles with machine learning (ML) to rapidly predict the gas sensitivity of materials. Initially, a comprehensive database of multi-physical parameters was established by modeling various adsorption sites on the surface of WO3, which serves as a representative material. Since density functional theory (DFT) is one of the first principles, DFT calculations were conducted to derive essential multi-physical parameters, including bandgap, density of states (DOS), Fermi level, adsorption energy, and structural modifications resulting from adsorption. The collected data was subsequently utilized to develop a correlation model linking the multi-physical parameters to gas sensitive performance using intelligent algorithms. The model’s performance was assessed through receiver operating characteristic (ROC) curves, confusion matrices, and other evaluation metrics, ultimately achieving a prediction accuracy of 90% for identifying key features influencing gas adsorption performance. This proposed strategy for predicting the gas sensitive characteristics of materials holds significant potential for application in identifying additional gas sensitive properties across various materials.

Keywords

• Machine learning
• Density functional theory
• Rapid assessment
• Gas sensor