Modeling and analysis of droplet generation in microchannels using interpretable machine learning methods DOI
Mengqi Liu, Haoyang Hu, Yongjin Cui

et al.

Chemical Engineering Journal, Journal Year: 2025, Volume and Issue: unknown, P. 161972 - 161972

Published: March 1, 2025

Language: Английский

A Multi-Method Approach to Analyzing MOFs for Chemical Warfare Simulant Capture: Molecular Simulation, Machine Learning, and Molecular Fingerprints DOI Creative Commons

Z. H. Ming,

Min Zhang, Shouxin Zhang

et al.

Nanomaterials, Journal Year: 2025, Volume and Issue: 15(3), P. 183 - 183

Published: Jan. 24, 2025

Mustard gas (HD) is a well-known chemical warfare agent, recognized for its extreme toxicity and severe hazards. Metal–organic frameworks (MOFs), with their unique structural properties, show significant potential HD adsorption applications. Due to the hazards of HD, most experimental studies focus on simulants, but molecular simulation research these simulants remains limited. Simulation analyses can uncover structure–performance relationships enable validation, optimizing methods, improving material design performance predictions. This study integrates simulations, machine learning (ML), fingerprinting (MFs) identify MOFs high simulant diethyl sulfide (DES), followed by in-depth analysis comparison. First, are categorized into Top, Middle, Bottom materials based efficiency. Univariate analysis, learning, then used compare distinguishing features fingerprints each category. helps optimal ranges Top materials, providing reference initial screening. Machine feature importance combined SHAP identifies key that significantly influence model predictions across categories, offering valuable insights future design. Molecular fingerprint reveals critical combinations, showing optimized when such as metal oxides, nitrogen-containing heterocycles, six-membered rings, C=C double bonds co-exist. The integrated using HTCS, ML, MFs provides new perspectives designing high-performance demonstrates developing CWAs simulants.

Language: Английский

Citations

0
Discovery of metal-organic frameworks for efficient NF3/N2 separation by integrating high-throughput computational screening, machine learning, and experimental validation DOI
Yanjing He, Zhi Fang, Weijiang Xue

et al.

Separation and Purification Technology, Journal Year: 2025, Volume and Issue: unknown, P. 132481 - 132481

Published: March 1, 2025

Language: Английский

Citations

0
Modeling and analysis of droplet generation in microchannels using interpretable machine learning methods DOI
Mengqi Liu, Haoyang Hu, Yongjin Cui

et al.

Chemical Engineering Journal, Journal Year: 2025, Volume and Issue: unknown, P. 161972 - 161972

Published: March 1, 2025

Language: Английский

Citations

0