Research Progress of Machine Learning in Surfactant Performance Prediction and Design

doi:10.6023/cjoc202512023

Abstract

Surfactants, as a class of important functional compounds, are widely used in daily chemicals, biomedicine, petroleum extraction, and other fields. However, the traditional trial-and-error experimental approach not only struggles to meet the performance requirements of novel surfactants under extreme conditions but also lacks systematic evaluation of their environmental fate and biosafety. Machine learning, as a data-driven research paradigm, offers a novel approach for efficient surfactant development, demonstrating significant potential particularly in molecular property prediction. This article systematically reviews advances in machine learning for predicting key surfactant properties (e.g., critical micelle concentration, surface tension, hydrophilic-lipophilic balance, adsorption efficiency, and Krafft point). Furthermore, it examines preliminary applications and prospects of this technology in predicting environmental safety parameters such as biodegradability and ecotoxicity. Current research predominantly focuses on predicting critical micelle concentration, surface tension, and hydrophilic-lipophilic balance, whereas modeling studies on adsorption efficiency, Krafft point, and environmental safety parameters remain relatively limited. By extending predictive capabilities to these underexplored properties, machine learning is poised to become a pivotal tool for rational design, performance optimization, and green assessment of surfactants, thereby advancing the development of high-performance and environmentally friendly surfactants.

Key words: Machine learning, surfactant, property prediction, molecular representation, QSPR

Cite this article

Guiping Zhang, Changhua Ding, Yong Guo, Yao Li, Xiaosong Xue. Research Progress of Machine Learning in Surfactant Performance Prediction and Design[J]. Chinese Journal of Organic Chemistry, 2026, 46(6): 2310-2326.

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Fig. & Tab. 12

Figure 1. Schematic diagram of five core properties of surfactants

Figure 2. Three molecular descriptors illustrated using sodium dodecyl sulfate (SDS) surfactant

Authors	Year	Algorithm	Features/Descriptors	Dataset size	Surfactant type	R² (Test)	RMSE (log CMC, mol/L) (Test)
Specific-type models
Gaudin et al.^[13]	2016	MLR	Integral; topological, compositional, and fragment descriptors	83	Sugar-based	0.910	0.320
Wang et al.^[45]	2019	MLR	Norm descriptors	101	Anionic	0.913	0.257
Jiao et al.^[46]	2020	PLS	Holographic fragment fingerprints	120	Gemini (anionic/ cationic)	0.980	0.176
Setiawan et al.^[47]	2021	Consensus	Dragon/CDK/ISIDA descriptors	231	Gemini cationic	0.870	0.350
Creton et al.^[48]	2022	SVM	Functional group descriptors	254^a	PFAS+conven- tional surfactants	0.899	0.273
General models
Zavala et al.^[49]	2021	GCN	Molecular graphs	202	All typesᵈ	0.920	0.300
Seddon et al.^[50]	2022	XGB	3D descriptors+physical constraints	154	All types	0.870	-
Striolo et al.^[30]	2023	GPs-GNN	Molecular graphs	202+43	All types	-	0.210
Boukelkal et al.^[51]	2024	DA-SVR	Dragon/Mordred descriptors+temperature	593	All types	0.986	0.144
Mitsos et al.^[52]	2024	GNN	Molecular graphs	429	All types	0.940	0.280
Mitsos et al.^[53]	2024	GNN	Molecular graphs+ temperature	1375^b	All types (multi-task)	0.950	0.240
Ge et al.^[54]	2024	LGBM (ionic)+ GBDT (nonionic)	Descriptors+ temperature+PCA classification	779	All types	0.944	0.284
Robinson et al.^[55]	2025	AttentiveFP	Molecular graphs	1395	All types (multi-task)	—	0.346
Complex conditions & mixture systems
Ham et al.^[56]	2024	GNN	Molecular graphs, MD simulations, geometric descriptors	92	All types (multi-task)	0.900	0.280
Barbosa et al.^[57]	2025	FNN	DFT-derived features+ temperature	1377	All types	0.950	0.380
Mitsos et al.^[58]	2025	Combined-GNN	Hybrid graphs, mole fraction, temperature	1924^c	Binary mixtures	0.930	0.249
Choudhary et al.^[59]	2025	ANN	Descriptor fusion	979	Binary mixtures	0.941	0.315

Table 1. Task and results of critical micelle concentration prediction

Figure 3. Predictive examples of unknown molecules by machine learning methods based on a dataset of 154 surfactants (a) C11E7, (b) β-GlcOC8+2, (c) 1-O-dodecyl-β-D-maltoside, and (d) Na- AOT.[50]

Figure 4. GCN predictions for all classes of surfactants Left: low-dimensional distribution of surfactant fingerprints using t-SNE. The test samples (red crosses) are widespread, and most of the designed surfactants (green points) fall outside the clusters of existing dataset. Right: parity plot between the predicted and experimental log CMC values (training data in blue and test data in red). The test set achieved a coefficient of determination (R²) of 0.92 with a root mean square error (RMSE) of 0.30 (log scale). Molecular structures are shown for the selected extreme points: Structure a is an anionic surfactant (minor outlier) with a high log CMC value; Structure b is a cationic surfactant (minor outlier) with a high log CMC value; Structure c is a zwitterionic surfactant (major outlier); Structure d is a nonionic surfactant with a low log CMC value.[49] Copyright (2021) American Chemical Society.

Figure 5. Multiscale feature importance analysis of surface tension prediction models for mixed surfactants (a) Feature importance ranking based on XGBoost model weights, displaying the top 15 key features; (b) Permutation importance analysis (PEI) quantifying feature impacts on model performance (MSE) through 100 Monte Carlo perturbations; (c) SHAP value analysis elucidating both direction and magnitude of marginal contributions of features to predictions.[77] Copyright (2025) Elsevier B.V.

Authors	Year	Algorithm	Features/Descriptors	Dataset size	Data description	R² (Test)	RMSE (mN/m) (Test)
Gaudin et al.^[73]	2018	MLR	Quantum chemical descriptors	70	Sugar-based nonionic surfactants (cyclic/non-cyclic head groups; linear/branched/unsaturated chains)	0.780	2.400
Hemmati-Sarapardeh et al.^[76]	2023	GBRT	Temperature, n-alkane molecular weight, concentration, HLB, PIT	390	Five ionic surfactants (C10TAB, C12TAB, C14TAB, C16TAB, SDS)	0.985	1.628
Pradilla et al.^[67]	2023	RF	37 manually defined molecular descriptors	691+9	691 conventional surfactants+ 9 amino acids	0.550	4.720
Saeedi Dehaghani et al.^[74]	2023	SGBT	Temperature, mole fraction, molecular weight, density, boiling point, etc.	4010	122 binary mixtures (48 ionic liquids+20 nonionic liquids)	0.993	0.001
Ham et al.^[56]	2024	GNN	Molecular graphs, MD simulations, geometric descriptors	92	Anionic, cationic, nonionic, and zwitterionic surfactants	0.900	2.640
Robinson et al.^[55]	2025	Attentive FP	Molecular graphs	972	Anionic, cationic, nonionic, and zwitterionic surfactants	–	3.407
Ge et al.^[77]	2025	XGBoost	Mole fraction, log concentration, interaction parameters, molecular descriptors	1135	Polyether nonionic surfactants+four representative surfactants (SDS, CTAB, TX100, BS12)	0.999	0.260^a

Table 2. Prediction task and results of surface tension at critical micelle concentration

Figure 6. Feature selection and importance analysis (a) Feature correlation heatmap analysis; (b) Cumulative variance contribution rate of principal component analysis (The cumulative contribution rate of the first nine principal components exceeds 90%, while increasing the number of principal components yields no significant improvement, indicating that the first nine principal components are sufficient to retain the vast majority of data information); (c) SHAP feature importance evaluation; (d) LOFO analysis revealing contributions and synergistic effects of key descriptors (ETA_C_A, MATS7p, etc.).[82] Copyright (2025) Elsevier B.V.

Figure 7. 3D visualization of structure-efficiency relationship of surfactants The clustering results based on PCA dimensionality reduction reveal an increasing trend of pC20 values along the arrow direction. Zones A, B, and C correspond to low, medium, and high adsorption efficiencies, respectively, with structural modifications (e.g., chain length variation, headgroup type) governing the spatial distribution patterns of molecules.[82] Copyright (2025) Elsevier B.V.

Figure 8. Applicability domain analysis of QSPR model predicting HLB values of nonionic surfactants[87] Copyright (2020) Elsevier B.V.

Figure 9. Decision tree prediction model for the Krafft temperature (TK>25 °C) of glycoside-based surfactants based on holistic molecular descriptor modeling

Figure 10. Pareto front for the first case study[101] Both SAscore and log(CMC), where CMC is in mg/L, are minimized and biodegradability maximized. The red points represent the optimal solutions

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