AI Big-Data Mining Empowered by MOFid for High-Performance Chemical Warfare Agent Adsorbents

doi:10.6023/A25080275

Acta Chimica Sinica ›› 2026, Vol. 84 ›› Issue (1): 8-19.DOI: 10.6023/A25080275 Previous Articles Next Articles

Article

基于MOFid赋能下的AI大数据挖掘高性能化学战剂吸附材料

翁惠琼^a^,^†, 黄河^a^,^†, 王雯菲^a, 李和国^b, 李晓鹏^b, 张守鑫^b, 李树华^a, 赵越^b^,^*(), 吴玉芳^a^,^*(), 乔智威^a^,^*()

^a 广州大学化学化工学院, 能源与催化研究所广东广州 510006
^b 核生化灾害防护化学全国重点实验室北京 102205

投稿日期:2025-08-08 发布日期:2025-10-23
基金资助:
国家自然科学基金项目(22478085); 国家自然科学基金项目(22308069); 国家自然科学基金项目(21978058); 广东省自然科学基金项目(2023A1515240076); 广东省自然科学基金项目(2022A1515011446); 新型反应器与绿色化学工艺湖北省重点实验室开放基金(NRG202407); 广州大学研究生创新能力培养项目(JCCX2024-064)

AI Big-Data Mining Empowered by MOFid for High-Performance Chemical Warfare Agent Adsorbents

Huiqiong Weng^a, He Huang^a, Wenfei Wang^a, Heguo Li^b, Xiaopeng Li^b, Shouxin Zhang^b, Shuhua Li^a, Yue Zhao^b^,^*(), Yufang Wu^a^,^*(), Zhiwei Qiao^a^,^*()

^a Guangzhou Key Laboratory for New Energy and Green Catalysis, College of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, Guangdong, China
^b State Key Laboratory of Chemistry for NBC Hazards Protection, Beijing 102205, China

Received:2025-08-08 Published:2025-10-23
Contact: * E-mail: zy291857261@126.com;yufang.wu@gzhu.edu.cn;zqiao@gzhu.edu.cn
About author:
† These authors contributed equally to this work.
Supported by:
National Natural Science Foundation of China(22478085); National Natural Science Foundation of China(22308069); National Natural Science Foundation of China(21978058); Natural Science Foundation of Guangdong Province(2023A1515240076); Natural Science Foundation of Guangdong Province(2022A1515011446); Open Fund of the Hubei Key Laboratory of Novel Reactor and Green Chemical Technology(NRG202407); Graduate Student Innovation Ability Training Program of Guangzhou University(JCCX2024-064)

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Abstract

To efficiently capture low-concentration chemical warfare agents (CWAs), which pose severe threats to human health and the environment, this study employed an AI big-data-driven high-throughput computational screening strategy to systematically analyze and evaluate the capture and separation performance of tens of thousands of metal-organic framework (MOF) adsorbents for trace CWAs in air. Grand Canonical Monte Carlo simulations were employed to evaluate the CWAs uptake of 15333 Computation-ready experimental MOFs under a gas mixture containing N₂, O₂, and toxic gas at 298 K and 101.325 kPa. A trade-off score (TSN) combining selectivity and adsorption capacity was introduced as a comprehensive performance metric. Predictive models were constructed using six algorithms (Decision Tree, Random Forest, Gradient Boosting Regression Tree, Extreme Gradient Boosting (XGB), Backpropagation Neural Network, and Light Gradient Boosting Machine) based on seven key descriptors of MOFs—namely, Largest Cavity Diameter (LCD), density of MOF (ρ_MOF), Pore-Limiting Diameter (PLD), porosity (φ), Volumetric Surface Area (VSA), Henry Coefficient (K), and heat of adsorption (Q⁰_st). And the XGB algorithm yielded the best predictive performance, with the R² up to 0.923. SHapley Additive exPlanations analysis revealed that K was the most critical descriptor for CWAs adsoprtion, with optimal φ and VSA ranges for maximal sarin and soman adsorption being 0.7~0.8 and 2000~2500 m²•cm^-3, respectively. Then, the structural commonalities of the top 1% high-performance MOFs were analyzed by integrating the XGB algorithm with the MOFid standardized identifier. The results reveal that the synergistic effect between open transition metal sites (particularly Cr, Nb, Al, In, Li) and rigid organic linkers of MOFs enhances the adsorption affinity towards CWAs. Meanwhile, the high occurrence probability of specific topological structures (such as sql and kgm) indicates that they are conducive to forming favorable pore structures, thus enhancing the adsorption effect. Guided by these insights, 26 novel MOFs were rational designed for enhanced CWAs capture. This study, employing AI big-data mining driven by MOFid method, provides critical guidance for optimizing MOF adsorption performance and screening highly efficient materials, thereby facilitating the efficient capture of trace chemical warfare agents in air and advancing the development of protective equipment.

Key words: AI big-data mining, MOFid, high-throughput screening, chemical warfare agents, machine learning, molecular simulation, metal-organic frameworks

Cite this article

Huiqiong Weng, He Huang, Wenfei Wang, Heguo Li, Xiaopeng Li, Shouxin Zhang, Shuhua Li, Yue Zhao, Yufang Wu, Zhiwei Qiao. AI Big-Data Mining Empowered by MOFid for High-Performance Chemical Warfare Agent Adsorbents[J]. Acta Chimica Sinica, 2026, 84(1): 8-19.

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Fig. & Tab. 8

Figure 1. Structure-performance relationships for the adsorption of sarin and soman (a) Nsarin-φ; (b) Nsarin-LCD; (c) Nsarin-ρ; (d) Nsarin-K; (e) Nsoman-φ; (f) Nsoman-LCD; (g) Nsoman-ρ; (h) Nsoman-K. The color represents the value of TSN. Each figure contains the data of MOFs

Figure 2. Structure-performance relationships for the selectivity of sarin and soman (a) Ssarin-φ; (b) Ssarin-LCD; (c) Ssarin-ρ; (d) Ssarin-K; (e) Ssoman-φ; (f) Ssoman-LCD; (g) Ssoman-ρ; (h) Ssoman-K. The color represents the value of TSN. Each figure contains the data of MOFs

气体	描述符	吸附量N/(mol•kg^-1)	选择性S
梭曼、沙林	φ	0.71~0.78	0.22~0.90
	VSA/(m²•cm^-3)	2446~2647	328~1160
	LCD/nm	0.664~0.918	0.496~0.755
	PLD/nm	0.542~0.868	0.262~0.589
	ρ/(kg•m^-3)	421~635	915~1953
	K/(mol•kg^-1•Pa^-1)	0.18×10^-2~1.5×10⁷	1.73×10^-3~1.13×10³
	Q⁰_st/(kJ•mol^-1)	19.37~86.02	24.52~68.22
DIFP	φ	0.67~1.02	0.14~0.73
	VSA/(m²•cm^-3)	1065~3290	334.3~1099.7
	LCD/nm	0.665~1.336	0.504~1.164
	PLD/nm	0.493~1.362	0.219~1.164
	ρ/(kg•m^-3)	392.7~690.9	1125.3~3260.5
	K/(mol•kg^-1•Pa^-1)	0.0001~5.33×10⁶	0.12~5.8×10¹¹
	Q⁰_st/(kJ•mol^-1)	24.19~85.41	45.20~105.73
DMMP	φ	0.67~1.14	0.13~0.64
	VSA/(m²•cm^-3)	1065 ~3290	309.4~2383.7
	LCD/nm	0.664~0.930	0.433~0.787
	PLD/nm	0.493~1.092	0.242~0.659
	ρ/(kg•m^-3)	392.72~808.97	728.76~3370.18
	K/(mol•kg^-1•Pa^-1)	0.62~9.41×10⁶	0.003~2.28×10⁹
	Q⁰_st/(kJ•mol^-1)	55.69~228.24	46.63~203.89

Table 1. Optimal ranges of different descriptors for MOFs adsorbing four toxic agents

Figure 3. Comparison of the simulated TSN and the predicted TSN obtained from six ML algorithms (a) DT; (b) RF; (c) XGB; (d) GBR; (e) BPNN; (f) LGBM. The dots represent the number of MOFs

Figure 4. Evaluation criteria of six ML algorithms on training and test sets

Figure 5. Global feature importance (left, bar charts) and local explanation summary (right, beeswarm plots), in which a dot represents an individual data point in this study Note: The dot on the x-axis shows how each data point impacts the model’s prediction for each feature, and when multiple dots are at the same x position, they show the density (Colors represent eigenvalues, where red dots represent high and blue dots represent low; positive SHAP values indicate a positive feature effect on the predicted value, and negative SHAP values indicate a negative feature effect on the predicted value)

Figure 6. The number and probability of different metals in TOP1% MOFs having excellent performance (a) soman; (b) sarin; (c) DMMP; (d) DIFP adsorption properties

毒剂	Linker			相应次数	Topology	次数
sarin	QMKYBPDZANOJGF C₉H₆O₆	OYFRNYNHAZOYNF C₈H₆O₆	LSHYAWDVBASRBW PO₄	8/5/4	srs	13
DIFP	GZJYHAYPVZNUJX C₈H₆O₄	SATWKVZGMWCXOJ C₂₇H₁₈O₆	QMKYBPDZANOJGF C₉H₆O₆	8/5/4	sql	14
soman	GZJYHAYPVZNUJX C₈H₆O₄	QMKYBPDZANOJGF C₉H₆O₆	KKEYFWRCBNTPAC C₈H₆O₄	16/5/3	kgm	12
DMMP	GZJYHAYPVZNUJX C₈H₆O₄	QMKYBPDZANOJGF C₉H₆O₆	KKEYFWRCBNTPAC C₈H₆O₄	15/8/3	sql	15

Table 2. Critical structural properties governing MOF adsorption of four toxic agents

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基于MOFid赋能下的AI大数据挖掘高性能化学战剂吸附材料

AI Big-Data Mining Empowered by MOFid for High-Performance Chemical Warfare Agent Adsorbents

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