Solvents are widely used in chemical processes. The use of efficient model‐based solvent selection techniques is an option worth considering for rapid identification of candidates with better economic, environment and human health properties. In this paper, an optimization‐based MLAC‐CAMD framework is established for solvent design, where a novel machine learning‐based atom contribution method is developed to predict molecular surface charge density profiles (σ‐profiles). In this method, weighted atom‐centered symmetry functions are associated with atomic σ‐profiles using a high‐dimensional neural network model, successfully leading to a higher prediction accuracy in molecular σ‐profiles and better isomer identifications compared with group contribution methods. The new method is integrated with the computer‐aided molecular design technique by formulating and solving a mixed‐integer nonlinear programming model, where model complexities are managed with a decomposition‐based strategy. Finally, two case studies involving crystallization and reaction are presented to highlight the wide applicability and effectiveness of the MLAC‐CAMD framework.

中文翻译：

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基于机器学习的原子贡献方法，用于预测表面电荷密度分布和溶剂设计

溶剂广泛用于化学过程中。快速识别具有更好的经济，环境和人类健康特性的候选人时，值得考虑使用基于模型的有效溶剂选择技术。本文针对溶剂设计建立了基于优化的MLAC-CAMD框架，其中开发了一种基于机器学习的新颖原子贡献方法来预测分子表面电荷密度分布（σ分布）。在这种方法中，使用高维神经网络模型将加权原子中心对称函数与原子σ轮廓关联，成功地提高了分子σ的预测精度相较于基团贡献方法，其谱图和更好的异构体鉴定。通过制定和求解混合整数非线性规划模型，该新方法与计算机辅助分子设计技术相集成，该模型采用基于分解的策略来管理模型复杂性。最后，提出了两个涉及结晶和反应的案例研究，以强调MLAC-CAMD框架的广泛适用性和有效性。