COSMO‐based computer‐aided molecular/mixture design: A focus on reaction solvents

In this article, we investigate reaction solvent design using COSMO‐RS thermodynamics in conjunction with computer‐aided molecular design (CAMD) techniques. CAMD using COSMO‐RS has the distinct advantage of being a method based in quantum chemistry, which allows for the incorporation of quantum‐level information about transition states, reactive intermediates, and other important species directly into CAMD problems. This work encompasses three main additions to our previous framework for solvent design (Austin et al., Chem Eng Sci. 2017;159:93–105): (1) altering the group contribution method to estimate hydrogen‐bonding and non‐hydrogen‐bonding σ‐profiles; (2) ab initio modeling of strong solute/solvent interactions such as H‐bonding or coordinate bonding; and (3) solving mixture design problems limited to common laboratory and industrial solvents. We apply this methodology to three diverse case studies: accelerating the reaction rate of a Menschutkin reaction, controlling the chemoselectivity of a lithiation reaction, and controlling the chemoselectivity of a nucleophilic aromatic substitution reaction. We report improved solvents/mixtures in all cases. © 2017 American Institute of Chemical Engineers AIChE J, 63: 104–122, 2018     

Efficient ant colony optimization for computer aided molecular design: Case study solvent selection problem

In this paper, we propose a novel computer-aided molecular design (CAMD) methodology for the design of optimal solvents based on an efficient ant colony optimization (EACO) algorithm. The molecular design problem is formulated as a mixed integer nonlinear programming (MINLP) model in which a solvent performance measure is maximized (solute distribution coefficient) subject to structural feasibility, property, and process constraints. In developing the EACO algorithm, the better uniformity property of Hammersley sequence sampling (HSS) is exploited. The capabilities of the proposed methodology are illustrated using a real world case study for the design of an optimal solvent for extraction of acetic acid from waste process stream using liquid–liquid extraction. The UNIFAC model based on the infinite dilution activity coefficient is used to estimate the mixture properties. New solvents with better targeted properties are proposed.     

A theoretical study of solvent effects on Kolbe-Schmitt reaction kinetics

A theoretical DFT study was employed to confirm the Kolbe-Schmitt reaction mechanism and investigate solvent effects on this reaction. The use of a solvent in the Kolbe-Schmitt reaction is desirable to facilitate a homogeneous reaction mixture and potentially improve the reaction rate. The candidate solvents were designed using computer aided molecular design (CAMD) and tested using DFT solvation calculations. The results from the quantum mechanical calculations were then used to determine the rate constants for each elementary step, the overall reaction yields and the corresponding residence time. The methodology was tested on the reaction without solvent, with solvents reported in the literature, and with the designed solvents. The study revealed that in the presence of solvents with high dielectric constant the reaction becomes reversible, leading to low product yields.     

A computer-aided molecular design framework for crystallization solvent design

One of the key decisions in designing solution crystallization processes is the selection of solvents. In this paper, we present a computer-aided molecular design (CAMD) framework for the design and selection of solvents and/or anti-solvents for solution crystallization. The CAMD problem is formulated as a mixed integer nonlinear programming (MINLP) model. Although, the model allows any combination of performance objectives and property constraints, in the case studies, potential recovery was considered as the performance objective. The latter, needs to be maximized, while other solvent property requirements such as solubility, crystal morphology, flashpoint, toxicity, viscosity, normal boiling and melting point are posed as constraints. All the properties are estimated using group contribution methods. The MINLP model is then solved using a decomposition approach to obtain optimal solvent molecules. Solvent design and selection for two types of solution crystallization processes namely cooling crystallization and drowning out crystallization are presented. In the first case study, the design of single compound solvent for crystallization of ibuprofen, which is an important pharmaceutical compound, is addressed. One of the important issues namely, the effect of solvent on the shape of ibuprofen crystals is also considered in the MINLP model. The second case study is a mixture design problem where an optimal solvent/anti-solvent mixture is designed for crystallization of ibuprofen by the drowning out technique. For both case studies the performance of the solvents are verified qualitatively through SLE diagrams.     

考虑选择性和反应速率的多目标制药反应溶剂设计

药物研制过程存在着大量的液–液均相有机反应,合适的反应溶剂能够大幅提高此类反应的反应速率与选择性,从而提高合成效率,提升药物质量。以2,4-二氯-5-硝基嘧啶与对氨基苯腈的芳香亲核反应（S_NAr）为研究对象,采用计算机辅助分子设计（computer-aided molecular design, CAMD）的方法进行反应溶剂设计。首先使用量子力学（quantum mechanics, QM）计算的方法获得少量溶剂中的反应速率常数并通过反应动力学模型与溶剂性质关联,然后构建同时考虑选择性和反应速率常数的混合整数非线性规划（mixed-integer nonlinear programming, MINLP）的多目标优化模型,最后采用分解式算法对模型优化求解,实现制药反应溶剂设计的目标。     

A molecular design method based on the COSMO‐SAC model for solvent selection in ionic liquid extractive distillation

In this study, a molecular design method was used to select solvents for extractive distillation. A COSMO‐SAC model was used to screen for prospective solvents from a wide variety of ionic liquids for extractive distillation. Based on the COSMO‐SAC model, the σ‐profile database of ILs was established. Selectivity and solubility were used as the indexes for solvent screening. According to the molecular design method, three suitable extractive distillation solvents were determined for acetonitrile‐water and ethanol‐cyclohexane systems. Vapor ‐ liquid equilibrium experiment were used to test chosen ILs. This study showed that the experimental and design results were consistent with each other. Therefore, this method is effective and applicable to pick ILs solvents for extractive distillation, and the results could provide a theoretical foundation for industrial production. © 2016 American Institute of Chemical Engineers AIChE J, 62: 2853–2869, 2016     

基于Dragon描述符与改进的决策树-遗传算法的反应溶剂设计方法

反应溶剂被广泛应用于液-液均相有机合成中，能够大幅度提高反应速率与选择性，有助于绿色合成新工艺路线的开发。提出了一种基于Dragon描述符与SMILES (simplified molecular-input line-entry system)编码的计算机辅助(computer-aided molecular design, CAMD)反应溶剂设计方法。首先，利用决策树-遗传算法构建可定量预测反应速率常数k的反应动力学模型；基于构建的反应动力学模型，提出了集成决策树-遗传算法与CAMD设计方法，通过SMILES分子编码算法生成同分异构体，并利用Dragon软件计算描述符大小，建立由目标函数与约束方程组成的混合整数非线性规划(mixed integer nonlinear programming, MINLP)模型，进一步采用分解算法对模型进行优化求解，从而实现反应溶剂设计目标；最后，以Diels-Alder反应为例，验证了该方法的可行性与有效性。     

Computer-aided molecular design in the continuous-molecular targeting framework using group-contribution PC-SAFT

Computer-aided molecular design allows generating novel fluids fulfilling a set of target properties. An integrated design of fluid and process directly employs a process-based objective function. In this work, we solve the integrated process and fluid design problem using the continuous-molecular targeting computer-aided molecular design (CoMT–CAMD) framework. CoMT–CAMD exploits the molecular picture underlying the PC-SAFT equation of state. In the simultaneous optimization of process and fluid, relaxed pure component parameters allow for an efficient optimization. The result is a hypothetical optimal target fluid. In previous work, fluids showing similar performance as the target fluid were obtained from a mapping onto a database. Here, we integrate computer-aided molecular design to realize the actual design of novel fluids. The resulting method for fluid design is based on a group-contribution method for the PC-SAFT parameters (GPC-SAFT) and applied to the design of working fluids for Organic Rankine cycles and solvents for CO₂ capture.     

基团贡献法分子设计研究的进展

利用基团贡献法可预测化合物的性质,还能用于化合物的计算机辅助分子设计(CAMD).本文论述了基于基团贡献法CAMD的基本原理,以及在溶剂和聚合物等领域分子设计的应用,对分子设计的计算方法也作了简单的介绍.随着绿色溶剂和新型聚合物材料需求的增加,基团贡献法CAMD将大有应用前景.     

Outer approximation algorithm with physical domain reduction for computer‐aided molecular and separation process design

Integrated approaches to the design of separation systems based on computer‐aided molecular and process design (CAMPD) can yield an optimal solvent structure and process conditions. The underlying design problem, however, is a challenging mixed integer nonlinear problem, prone to convergence failure as a result of the strong and nonlinear interactions between solvent and process. To facilitate the solution of this problem, a modified outer‐approximation (OA) algorithm is proposed. Tests that remove infeasible regions from both the process and molecular domains are embedded within the OA framework. Four tests are developed to remove subdomains where constraints on phase behavior that are implicit in process models or explicit process (design) constraints are violated. The algorithm is applied to three case studies relating to the separation of methane and carbon dioxide at high pressure. The process model is highly nonlinear, and includes mass and energy balances as well as phase equilibrium relations and physical property models based on a group‐contribution version of the statistical associating fluid theory (SAFT‐γ Mie) and on the GC⁺ group contribution method for some pure component properties. A fully automated implementation of the proposed approach is found to converge successfully to a local solution in 30 problem instances. The results highlight the extent to which optimal solvent and process conditions are interrelated and dependent on process specifications and constraints. The robustness of the CAMPD algorithm makes it possible to adopt higher‐fidelity nonlinear models in molecular and process design. © 2016 The Authors AIChE Journal published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers AIChE J, 62: 3484–3504, 2016     

Efficient integration of optimal solvent and process design using molecular clustering

We present a molecular clustering approach for the efficient incorporation of solvent design information into process synthesis in the integrated design of solvent/process systems. The approach is to be used in conjunction with an integrated solvent/process design approach where the solvent design stage utilises multi-objective optimisation in order to identify Pareto optimal solvent candidates that are subsequently evaluated in a process synthesis stage. We propose to introduce the solvent design information into the process synthesis stage through the use of molecular clusters. The partitioning of the original Pareto optimal set of solvents leads to smaller compact groups of similar solvent molecules from which representative molecules are introduced into the process synthesis model as discrete options to determine the optimal process performance associated with the optimal solvent. We investigate two clustering strategies, serial and parallel clustering, that allow to effectively exploit the solvent-process design interactions to minimise the computational demands of the process synthesis stage. We further propose a clustering heuristic probability that can aid decision making in clustering and can significantly accelerate the search for the best integrated solvent-process systems. The presented method is illustrated with case studies in the design of solvents for liquid-liquid extraction, gas-absorption and extractive distillation systems.     

基于高阶基团贡献法与COSMO-SAC模型的溶剂设计方法

提出了一种基于高阶基团贡献法与类导体屏蔽片段活度系数模型（conductor like screening model-segment activity coefficient, COSMO-SAC）的计算机辅助溶剂设计方法（computer-aided molecular design, CAMD）。首先,基于高阶基团贡献法（higher-order group contribution, GC⁺）与COSMO-SAC模型构建GC⁺-COSMO方法,关联分子基团组合与表面屏蔽电荷密度分布[σ-profiles, p(σ)]、分子空腔体积V_c,实现对二者的高通量预测;然后结合基于简化分子线性输入系统（simplified molecular input line entry system, SMILES）的异构体生成算法与GC⁺-COSMO方法实现CAMD技术对异构体的识别及性质区分;最后,通过目标函数与约束方程组成的混合整数非线性规划模型（mixed integer nonlinear programming, MINLP）来建立溶剂设计问题,进一步采用分解式算法优化求解,实现溶剂优化设计目标。基于以上模型和方法开展了狄尔斯-阿尔德（Diels-Alder, DA）竞争性反应溶剂设计,验证了提出的方法的可行性与有效性。     

基于COSMO-SAC模型的离子液体萃取剂的选择

COSMO-SAC模型是计算无限稀释活度因子的一种有效方法,只需知道分子结构,即可进行有机物或离子液体的无限稀释活度因子计算。COSMO-SAC模型中最耗时的计算步骤是产生σ-图谱(σ-profile)的量子化学计算。利用Materials Studio软件中的DMol3模块,建立了包含32种离子液体阴离子和191种离子液体阳离子的σ-图谱数据库。利用σ-图谱数据库和COSMO-SAC模型,针对离子液体液液萃取过程,提出了离子液体萃取剂的计算机辅助分子设计方法。以乙醇-乙酸乙酯体系为研究对象,选择了适宜的离子液体萃取剂,采用乙醇-乙酸乙酯-离子液体三元体系的液液平衡文献数据进行了验证。     

Rx-COSMO-CAMPD: Enhancing Reactions by Integrated Computer-Aided Design of Solvents and Processes based on Quantum Chemistry

Solvents strongly affect reaction-based chemical processes. Process design, therefore, needs to integrate solvent design. For this purpose, the integrated computer-aided molecular and process design (CAMPD) method Rx-COSMO-CAMPD is proposed. It employs a hybrid optimization scheme combining a genetic algorithm to explore the molecular design space with gradient-based optimization of the process. To overcome limitations of molecular design based on group-contribution methods, reaction kinetics and thermodynamic properties are predicted using advanced quantum-chemical methods. Rx-COSMO-CAMPD is demonstrated in a case study of a carbamate-cleavage process where promising solvents are designed efficiently. The results show that the integrated solvent and process design with Rx-COSMO-CAMPD outperforms computer-aided molecular design without process optimization in the identification of solvents that enable optimal process performance.     

一种简化的萃取溶剂分子设计方法

本研究提出一种新的萃取溶剂分子设计策略,首先以溶剂选择性为标准预选官能团,缩小分子设计的范围;再应用遗传算法对预选出的官能团进行组合,设计出符合要求的萃取溶剂分子。采用本设计的方法模拟计算了两个萃取溶剂设计实例,获得了令人满意的效果。     

The formulation of optimal mixtures with generalized disjunctive programming: A solvent design case study

Systematic approaches for the design of mixtures, based on a computer‐aided mixture/blend design (CAM^bD) framework, have the potential to deliver better products and processes. In most existing methodologies the number of mixture ingredients is fixed (usually a binary mixture) and the identity of at least one compound is chosen from a given set of candidate molecules. A novel CAM^bD methodology is presented for formulating the general mixture design problem where the number, identity and composition of mixture constituents are optimized simultaneously. To this end, generalized disjunctive programming is integrated into the CAM^bD framework to formulate the discrete choices. This generic methodology is applied to a case study to find an optimal solvent mixture that maximizes the solubility of ibuprofen. The best performance in this case study is obtained with a solvent mixture, showing the benefit of using mixtures instead of pure solvents to attain enhanced behavior. © 2016 The Authors AIChE Journal published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers AIChE J, 62: 1616–1633, 2016     

An experimental verification of morphology of ibuprofen crystals from CAMD designed solvent

In our previous work [Karunanithi et al., 2006. A computer-aided molecular design framework for crystallization solvent design. Chemical Engineering Science 61, 1247-1260] we proposed a computer-aided molecular design (CAMD) framework to design solvents for crystallization processes. One of the important aspects of that work was the consideration of a qualitative property, namely crystal morphology, along with other physico-chemical properties (quantitative) of the solvents within the modeling framework. However, it is our view that consideration of any qualitative property, such as morphology of crystals formed from solvents, necessitates additional experimental verification steps. In this work we report the experimental verification of crystal morphology for the case study, solvent design for ibuprofen crystallization, presented in Karunanithi et al. [2006. A computer-aided molecular design framework for crystallization solvent design. Chemical Engineering Science 61, 1247-1260]. This we believe is an important step for the validation of the proposed solvent design model.     

Graph machine learning for design of high-octane fuels

Fuels with high-knock resistance enable modern spark-ignition engines to achieve high efficiency and thus low CO₂ emissions. Identification of molecules with desired autoignition properties indicated by a high research octane number and a high octane sensitivity is therefore of great practical relevance and can be supported by computer-aided molecular design (CAMD). Recent developments in the field of graph machine learning (graph-ML) provide novel, promising tools for CAMD. We propose a modular graph-ML CAMD framework that integrates generative graph-ML models with graph neural networks and optimization, enabling the design of molecules with desired ignition properties in a continuous molecular space. In particular, we explore the potential of Bayesian optimization and genetic algorithms in combination with generative graph-ML models. The graph-ML CAMD framework successfully identifies well-established high-octane components. It also suggests new candidates, one of which we experimentally investigate and use to illustrate the need for further autoignition training data.