Machine learning perspective: Revealing deep mechanisms and new advances in adsorption and catalysis of gaseous molecules

Yue Zhu, Fengyu Gao, Lei Yi, Honghong Yi, Qingjun Yu, Shunzheng Zhao, Yuansong Zhou, Ya Wang and Xiaolong Tang

Applied Energy, 2025, vol. 396, issue C, No S0306261925009717

Abstract: In the field of chemical and environmental engineering, research on the adsorption and catalytic processes of gas molecules has long been constrained by the efficiency bottleneck of traditional trial and error methods and empirical analysis. With the rapid development of artificial intelligence technology, machine learning (ML), leveraging its powerful data processing and pattern recognition capabilities, has provided a revolutionary tool for material design and optimization. This paper systematically reviews the latest progress of ML in the research of gas-molecule adsorption and catalytic materials, focusing on three core aspects: formulation screening, material property correlation, and descriptor construction. By integrating experimental conditions and material characteristics, ML models can efficiently predict adsorption/catalytic performance and uncover key descriptors to guide the design of new materials. In formulation screening, ML can identify key factors from complex multivariable systems and optimize the composition, synthesis methods, and reaction conditions of catalysts and adsorbents. Regarding material property correlation, it can deeply analyze the internal relationships between the physical-chemical and reaction properties of materials. In the field of descriptor construction, innovative designs of electronic, elemental, and structural descriptors offer a new perspective for understanding material properties and catalytic reactions. Although ML shows great potential in this field, it also faces many challenges, such as data quality and scarcity, model interpretability and generalization ability, and descriptor universality. This paper not only reviews key application cases of ML in gas pollutant treatment but also proposes the urgent need for interdisciplinary collaboration and the construction of standardized databases. Despite being in the early stage of research, the ML-driven Materials Genome Initiative has demonstrated the potential to revolutionize traditional research and development models, opening up a new path for solving environmental and energy problems.

Keywords: Machine learning; Model and algorithm; Formulation screening; Material property correlation; Descriptors creation (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261925009717
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:396:y:2025:i:c:s0306261925009717

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2025.126241

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().