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。     

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。     

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。     

基于遗传算法的分子设计初探

本文介绍了一种基于遗传算法思想的计算机辅助分子设计（CAMD）方法，该法以DUNIFAC法基团形式进行基因编码，并对基础遗传算法中的选择，杂交和变异等算子进行了修正，同时提出了倒身和单性两个新的操作算子，最后用实例说明该方法的有效性。     

化工分离溶剂筛选算法的研究进展

本文首先阐述了化工分离溶剂筛选方法的研究进展,对文献中采用的优化算法进行了分析,指出了目前算法中的一些缺陷;本文首次将混合算法引入到溶剂的计算机筛选的领域中,提出了基于遗传模拟退火混合算法的计算机分子设计方法（CAMD）,避免了单种算法的缺点,为CAMD的进展和应用奠定了更好的基础。     

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.     

机器学习辅助的纳米催化反应动力学研究进展

动力学模拟是催化反应动力学研究的重要手段之一，有助于理解催化反应的内在机理，对于设计高效稳定的纳米催化剂十分重要。基于经验力场的经典分子动力学计算速度快，但计算精度有限。基于第一性原理的分子动力学方法精度高，但计算速度慢，难以大规模实施。近年来，机器学习力场(MLFF)方法被广泛应用于势能面的开发，基于MLFF的分子动力学(MLFF MD)方法兼顾计算速度与准确性，为催化反应动力学研究带来了新契机。本文首先回顾了MLFF势能面构造的主要方法，对基于对称函数的描述符设计原理和以嵌入式网络为基础的描述符构建方法进行了阐述，展示了MLFF MD方法应用于催化剂结构/组分演变和催化反应过程动力学模拟中的最新进展，进一步展望了MLFF在长时动力学模拟中所面临的挑战。     

改进遗传算法在Claus脱硫反应动力学研究中的应用

Claus工艺中硫化物会与气体中的甲烷反应生成少量的CS_2,从而对体系的反应速率产生影响。为了建立该复杂体系的完整宏观动力学以纠正反应器开发和放大过程中带来计算结果的偏差,本工作基于传统的Claus脱硫反应动力学模型,通过引入修正参数β来修正原料气中CS_2存在对反应速率的影响,并采用局部最优化算法与遗传算法相结合的算法对动力学模型参数进行了估值。统计检验和残差分析表明所建立的模型是适定的,估值结果是可信的。     

Group2vec：基于无监督机器学习的基团向量表示及其物性预测应用

定量构效关系模型在化工产品设计中发挥着重要作用。基于自然语言处理技术的深度学习建模方法是构建定量构效关系模型的有效方法之一。提出一种基于基团词嵌入模型(Group2vec)的深度学习物性预测框架。首先，建立数据库用于预训练与物性预测。其次，利用基团分割方法，将数据库中分子SMILES文本转化为基团序列。再次，通过CBOW算法将基团序列进行词嵌入预训练，获得包含相似性结构信息的基团向量。最后，基于基团向量构建包含注意力机制的深度学习模型，并在不同物性数据库上进行模型测试，同时将其与现有模型进行比较，对比结果表明基于Group2vec的深度学习物性预测模型不仅具有较高的预测准确性与通用性，也具备一定的可解释性。     

基于三种算法的瓜子壳热解动力学分析

采用热失重法研究瓜子壳在氮气气氛中的热解过程，结果显示：瓜子壳在50℃/min的升温速率下主要热失重区间为167~427℃，最大失重速率发生在354.42℃，达42.9%/min。将瓜子壳参与反应的物质分为伪纤维素、伪半纤维素和伪木质素，假设瓜子壳在热解过程中由3个独立互不影响的平行反应而成，分别运用遗传算法（GA）、非线性最小二乘法（NLS）、高斯拟合-遗传算法（GGA）建立瓜子壳热解动力学模型，对瓜子外壳在热解过程中的动力学参数求解。结果表明：GGA算法性能最优，模拟DTG曲线与实验数据偏离度最小，仅为1.12%；与GA相比，其迭代次数少且计算稳定；与NLS相比，其计算快、对初始值要求不高。     

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.     

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.     

Generic mathematical programming formulation and solution for computer-aided molecular design

This short communication presents a generic mathematical programming formulation for computer-aided molecular design (CAMD). A given CAMD problem, based on target properties, is formulated as a mixed integer linear/non-linear program (MILP/MINLP). The mathematical programming model presented here, which is formulated as an MILP/MINLP problem, considers first-order and second-order molecular groups for molecular structure representation and property estimation. It is shown that various CAMD problems can be formulated and solved through this model.     

Optimal design of solvents for extractive reaction processes

It is well known that solvents can have significant effects on rates and equilibrium compositions of chemical reactions. The computer‐aided molecular design (CAMD) of solvents for heterogeneous liquid phase reactions is challenging due to multiple solvent effects on reaction and phase equilibria. In this work, we propose a CAMD methodology based on a genetic algorithm (GA) for identifying optimal solvents for liquid phase reactions where the objective is to maximize the reaction equilibrium conversion. In particular, a novel molecular encoding method is introduced to facilitate the construction and evaluation of solvent molecules in a defined structure space. The reliability of the method for fast identification of optimal reaction solvents is demonstrated for a selected biphasic esterification reaction. The proposed approach opens up new perspectives for intensifying extractive reaction processes via the purposeful design of solvent molecules. © 2016 American Institute of Chemical Engineers AIChE J, 62: 3238–3249, 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.     

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.     

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