基于分子动力学模拟的结构性产品配方设计方法

doi:10.16085/j.issn.1000-6613.2025-0311

摘要/Abstract

摘要：

传统配方产品设计通常是基于经验和大量的实验。计算机辅助分子设计（CAMD）将配方设计问题转化为确定与目标产品功能相匹配的化学物质及其分子组成的问题，显著提高设计效率和准确性。然而，对于结构性产品（如洗涤剂、牙膏等），其性能主要由微观结构决定，且一些难以描述的性质（如泡沫性能）缺乏可量化的数据，导致定量构效关系（QSPR）模型的建立存在困难。针对这一问题，本文提出了一种结合分子动力学（MD）模拟与机器学习的计算机辅助配方设计方法。首先，利用分子动力学模拟研究配方产品中不同成分的微观行为及其形成的微观结构，揭示了成分间的相互作用机理。然后，从模拟结果中提取可量化的分子动力学描述符，结合贝叶斯神经网络构建QSPR模型，预测配方成分与产品性能之间的关系。最后，通过数学优化算法求解最优配方组成。本文以牙膏产品为例，设计了满足泡沫性能需求的配方。结果表明，该方法能够显著减少配方设计的搜索空间，提高设计效率和科学性，为配方设计指明了方向。未来，随着技术进步和市场需求的变化，计算机辅助设计方法将在化工领域发挥更加重要的作用。

关键词: 分子动力学模拟, 计算机辅助分子设计, 配方设计, 表面活性剂, 定量构效关系

Abstract:

Traditional formulated product design relies heavily on experience and extensive experimentation. Computer-aided molecular design (CAMD) transforms this process by identifying chemical substances and their molecular compositions which match the target product's functionality, greatly enhancing design efficiency and accuracy. However, for structural products (e.‍g., detergents, toothpaste), performance is mainly determined by microstructure, and quantifying certain properties (e.‍g., foaming) is challenging due to insufficient data, making it difficult to establish quantitative structure-property relationship (QSPR) models. To address this, this paper proposes a computer-aided formulation design method that combines molecular dynamics (MD) simulation with machine learning. MD simulation is first used to study the microscopic behavior of different ingredients and their formed microstructure in the formulated product, revealing the interaction mechanisms among components. Then, quantifiable MD descriptors are extracted from the simulation results to construct a QSPR model in combination with Bayesian neural network, predicting the relationship between formulation composition and product performance. Finally, a mathematical optimization algorithm is employed to solve for the optimal formulation composition. A toothpaste formulation case study is provided, showing that this method can significantly reduce the formulation design search space and enhance design efficiency and scientificity. It also indicates the direction for formulation design. As technology advances and market demands evolve, computer-aided design methods are expected to play a more important role in the chemical engineering field in the future.

Key words: molecular dynamics simulation, computer-aided molecular design, formulation design, surfactants, quantitative structure-property relationship

中图分类号:

TQ658.4⁺1

齐妍, 常昊, 张磊. 基于分子动力学模拟的结构性产品配方设计方法[J]. 化工进展, 2025, 44(8): 4341-4351.

QI Yan, CHANG Hao, ZHANG Lei. Structural product formulation design method based on molecular dynamics simulation[J]. Chemical Industry and Engineering Progress, 2025, 44(8): 4341-4351.

图/表 12

图1 计算机辅助配方设计模型框架

表1 产品的需求和对应的属性及获取属性数据的方法

产品需求	对应属性	方法
香味持久	香味扩散速率	经验规则，实验，MD
泡沫丰富	泡沫稳定性，发泡能力	经验规则，实验，MD，量子化学方法（QM）
清洁效果好	临界胶束浓度	经验规则，数据库，实验，基团贡献法
产品形态	亲水亲油平衡	数据库，实验，Griffin方法
环境友好	成分、分子量	数据库，基团贡献法
安全无毒	成分、分子量	数据库，基团贡献法
成本低	成分含量与价格	经验规则

表2 牙膏配方成分及含量

功能	原料	质量分数/%
发泡剂	十二烷基硫酸钠（SDS）	1~2
润湿剂	甘油（GLY）	10~50
润湿剂	聚乙二醇（PEG）	0~10
芳香剂	柠檬烯（PRM）	1~2
活性成分	氟化钠（NaF）	0.243
增白剂	焦磷酸二钠（DP）	4.170
pH调节剂	50%氢氧化钠（NaOH）溶液	2.300
溶剂	水（H₂O）	补充至100%

图2 泡沫的分子动力学模拟流程Etotal—总势能；Ebond—键伸缩能；Eangle—键角弯曲能；Edihedral—二面角扭转能；Enon-bonder—非键相互作用能

表3 模拟系统的细节

系统	nwater	nGLY	nPEG	nSDS	nN₂	nO₂	L_x ×L_y ×L_z /Å
50%GLY	93044	19184	0	3120	510	146	264×264×500
50%GLY-10%PEG	73415	19184	1083	3120	510	146	264×264×500

图3 分子动力学模拟结果

图4 系统平衡后密度分布

图5 系统平衡后SDS倾斜角分布

图6 系统平衡后SDS头基周围水分子的径向分布函数

图7 系统平衡后分子的均方位移

图8 QSPR模型框架及结果

表4 配方设计结果

序号	质量分数/%								$f o b j$
序号	SDS	GLY	PEG	PRM	NaF	DP	NaOH	H₂O	$f o b j$
1	1.86	9.80	12.74	1.86	0.243	4.17	2.3	67.027	133.5212 $× 10 - 5$
2	1.86	46.93	9.71	1.86	0.243	4.17	2.3	32.927	130.3287 $× 10 - 5$
3	1.86	26.65	7.92	1.86	0.243	4.17	2.3	54.997	130.1674 $× 10 - 5$
4	1.86	49.77	4.56	1.86	0.243	4.17	2.3	35.237	127.8932 $× 10 - 5$
5	0.97	46.90	9.70	0.97	0.243	4.17	2.3	34.747	125.2137 $× 10 - 5$

表4 配方设计结果

序号	质量分数/%								$f o b j$
序号	SDS	GLY	PEG	PRM	NaF	DP	NaOH	H₂O	$f o b j$
1	1.86	9.80	12.74	1.86	0.243	4.17	2.3	67.027	133.5212 $× 10 - 5$
2	1.86	46.93	9.71	1.86	0.243	4.17	2.3	32.927	130.3287 $× 10 - 5$
3	1.86	26.65	7.92	1.86	0.243	4.17	2.3	54.997	130.1674 $× 10 - 5$
4	1.86	49.77	4.56	1.86	0.243	4.17	2.3	35.237	127.8932 $× 10 - 5$
5	0.97	46.90	9.70	0.97	0.243	4.17	2.3	34.747	125.2137 $× 10 - 5$

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