Machine learning-assisted solvent molecule design for efficient absorption of ethanethiol

doi:10.11949/0438-1157.20231370

Abstract

Abstract:

To solve the problems of low organic sulfur removal efficiency, long solvent development cycle and high cost in the traditional amine elution desulfurization process, the quantitative structure-activity relationship (QSPR) model for ethanethiol solubility was established by using seven machine learning algorithms. Besides, the absorption mechanism of ethanethiol was elucidated by using the SHapley Additive exPlanations (SHAP) method and the virtual screening for candidate molecules was conducted to identify efficient solvents for the absorption removal of ethanethiol. Molar solubilities of ethanethiol in 14732 solvents, which cover a wide range of chemical space, were calculated by using the conductor-like screening model for real solvents (COSMO-RS). XGBoost was identified as the optimal algorithm for predicting the molar solubility of ethanethiol, having $R_{t e s t}^{2}$ of 0.66, RMSE of 1.22, and MAE of 0.84. The complexity of molecular structure, covalent bonding, and electron distribution in molecules were identified as the key factors for the molar solubility of ethanethiol. Four solvents, including 3-ethoxypropylamine, 3-diethylaminopropylamine, 1,4-dimethylpiperazine, and 3-butoxypropylamine were identified as potential solvents. The results of the equilibrium solubility determination experiments show that 3-butoxypropylamine has the best ethanethiol dissolution with Henry’s law constant of 37.34 kPa.

Key words: molecule design, machine learning, solubility, absorption

摘要：

针对传统胺洗脱硫工艺中有机硫脱除效率低，溶剂开发周期长、成本高等问题，利用7种机器学习算法建立了乙硫醇溶解度的定量构效关系模型，运用SHAP方法阐释了乙硫醇的吸收机理，对备选分子库进行了虚拟筛选，识别出高效吸收脱除乙硫醇的溶剂。基于COSMO-RS模型计算了14732种溶剂的乙硫醇溶解度，这些分子覆盖了广泛的化学空间；XGBoost算法在预测乙硫醇溶解度方面表现最佳，该算法的 $R_{t e s t}^{2}$ 为0.66，RMSE为1.22，MAE为0.84；分子结构的复杂程度、共价键分布、电荷分布是影响乙硫醇溶解能力的关键因素；确定了4种候选溶剂：3-乙氧基丙胺、3-二乙胺基丙胺、1,4-二甲基哌嗪和3-丁氧基丙胺；平衡溶解度测定实验的结果表明3-丁氧基丙胺的乙硫醇吸收性能最优，亨利常数为37.34 kPa。

关键词: 分子设计, 机器学习, 溶解度, 吸收

CLC Number:

TQ 028.8

Yuxiang CHEN, Chuanlei LIU, Zijun GONG, Qiyue ZHAO, Guanchu GUO, Hao JIANG, Hui SUN, Benxian SHEN. Machine learning-assisted solvent molecule design for efficient absorption of ethanethiol[J]. CIESC Journal, 2024, 75(3): 914-923.

陈宇翔, 刘传磊, 龚子君, 赵起越, 郭冠初, 姜豪, 孙辉, 沈本贤. 机器学习辅助乙硫醇高效吸收溶剂分子设计[J]. 化工学报, 2024, 75(3): 914-923.

Figures/Tables 10

Fig.1 Experimental apparatus for equilibrium solubility measurement

Table 1 Raw materials used for equilibrium solubility determination experiment

原料	含量	生产厂家
3-乙氧基丙胺	99.0%	北京华威锐科化工有限公司
3-丁氧基丙胺	98.0%	上海恩拿马生物科技有限公司
3-二乙胺基丙胺	99.0%	上海迈瑞尔生化科技有限公司
1,4-二甲基哌嗪	98.0%	上海迈瑞尔生化科技有限公司
环丁砜	99.7%	上海新铂化学技术有限公司
混合气		上海伟创标准气体有限公司
乙硫醇	5578.2 mg/m³（标准工况）
氮气	余量

Fig.2 Distribution of molecular weight, octanol-water partition coefficient, and molar solubility of ethanethiol in the initial training set, and the two-dimensional chemical space of the initial training set

Fig.3 The screening process of molecular descriptors

Table 2 Hyperparameter of different algorithms

算法	超参数	搜索空间	优化后的参数
MLR	default	—	default
RR	alpha	logspace(-5,3,20)	0.16
LR	alpha	logspace(-5,3,20)	0.12
KNN	n_neighbors	[2,4,8,16,32]	8
DT	max_depth	[1,10,100,1000,10000]	100
RF	max_depth	[1,10,100,1000,10000]	100
	max_features	[sqrt, log2, none]	sqrt
	n_estimators	[10,100,1000,10000]	10000
XGBoost	learning_rate	[0.001,0.01,0.1,1,10]	0.01
	max_depth	[1,10,100,1000]	100
	n_estimators	[10,100,1000,10000]	10000

Table 3 Metrics of ethanethiol solubility prediction model using different algorithms

算法	$R_{t r a i n}^{2}$	$R_{t e s t}^{2}$	RMSE	MAE
MLR	0.66	0.65	1.23	0.87
RR	0.66	0.65	1.24	0.87
LR	0.64	0.63	1.27	0.90
KNN	0.57	0.36	1.67	1.25
DT	0.75	0.48	1.50	1.04
RF	0.95	0.65	1.23	0.86
XGBoost	0.98	0.66	1.22	0.84

Table 3 Metrics of ethanethiol solubility prediction model using different algorithms

算法	$R_{t r a i n}^{2}$	$R_{t e s t}^{2}$	RMSE	MAE
MLR	0.66	0.65	1.23	0.87
RR	0.66	0.65	1.24	0.87
LR	0.64	0.63	1.27	0.90
KNN	0.57	0.36	1.67	1.25
DT	0.75	0.48	1.50	1.04
RF	0.95	0.65	1.23	0.86
XGBoost	0.98	0.66	1.22	0.84

Fig.4 Performance of the ethanethiol solubility prediction model

Fig.5 The feature importance and Shapley value of the top 20 important molecular descriptors

Table 4 Properties of the four selected molecules

分子名称	CAS号	沸点/℃	熔点/℃	水溶性
3-乙氧基丙胺	6291-85-6	136~138	-24	可溶
3-二乙胺基丙胺	104-78-9	168~171	-60	可溶
1,4-二甲基哌嗪	106-58-1	131~132	-1	可溶
3-丁氧基丙胺	16499-88-0	169~170	-65	可溶

Fig. 6 The experimental Henry’s constants of ethanethiol in 3-ethoxypropylamine, 3-diethylaminopropylamine, 1,4-dimethylpiperazine, 3-butoxypropylamine and sulfolane at 40℃

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