@article{Wang2026, 
author = {Mingyuan Wang and Yaqi Zhang and Bowen Xiong and Ke Wang and Xiangzhao Zhang and Jian Yang and Lin Xu and Guanjun Qiao and Guiwu Liu},
title = {First-principles calculations informing machine learning framework and visualization system for rapid and generalized gas response prediction in black phosphorus sensors},
year = {2026},
journal = {Journal of Advanced Ceramics},
volume = {15},
number = {3},
pages = {9221243},
keywords = {first-principles calculations, machine learning (ML), black phosphorus (BP), gas response, visualization system},
url = {https://www.sciopen.com/article/10.26599/JAC.2026.9221243},
doi = {10.26599/JAC.2026.9221243},
abstract = {Gas sensors are vital in practical applications, yet the efficient screening of sensing materials remains a formidable challenge. Conventional trial-and-error approaches are costly, single descriptors fail to capture complex interactions, and multiparameter combinations introduce nonlinearities. To overcome these limitations, we propose a synergistic strategy that integrates first-principles calculations with machine learning (ML) for the rapid prediction of gas sensitivity. Using black phosphorus (BP) as a model system, we evaluated its responsiveness to 21 gases by analyzing adsorption-induced electronic and structural changes. Key descriptors extracted from these calculations were used to train six ML models. The extra tree (ET) model demonstrated exceptional robustness, achieving 96% accuracy with minimal deviation in fivefold cross-validation and top-tier performance in F1-score evaluation. Furthermore, analyses of feature importance and SHapley Additive exPlanations (SHAP) identified the adsorption energy, p-orbital center, valence band maximum, conduction band minimum, and Fermi level as the dominant descriptors. We also developed a lightweight, Python-based prediction and visualization system. By inputting only these five key features obtained from first-principles calculations, this tool enables real-time assessment of BP’s response to various gas molecules. This integrated approach demonstrated significant potential for predicting material sensing properties and offers valuable theoretical guidance for designing gas sensors.}
}