Computer-aided molecular design of solvents for accelerated reaction kinetics

Abstract

ABSTRACT:Solvents crucially alter the rates and selectivity of liquid-phase organic reactions, often hindering the development of new synthetic routes or, if chosen wisely, facilitating routes by improving rates and selectivities. To address this challenge, a systematic methodology is proposed that quickly identifies improved reaction solvents by combining quantum mechanical computations of the reaction rate constant in a few solvents with a computer-aided molecular design (CAMD) procedure. The approach allows the identification of a high-performance solvent within a very large set of possible molecules. The validity of our CAMD approach is demonstrated through application to a classical nucleophilic-substitution reaction for the study of solvent effects, the Menschutkin reaction. The results are successfully validated via in-situ kinetic experiments. A space of 1341 solvents is explored in silico, but requiring quantum mechanical calculations of the rate constant in only 9 solvents, and uncovering a solvent that increases the rate constant by 40%.What is the best solvent for a given chemical reaction? Given that the rate and selectivity of chemical reactions can vary by several orders of magnitude in different solvents, 1,2 this question has important ramifications for the exploration of novel reaction routes and the development of industrial processes. 3,4 When investigating new liquid-phase reactions, it is essential to find a solvent that promotes the desired reaction without excessive catalyst deactivation, side-product formation, or solubility limitations. Indeed, a poor solvent choice can result in a missed opportunity to investigate novel chemistry or catalysts. At the process-development level, the problem of solvent choice is 2 compounded by the numerous safety, environmental and process constraints that must be satisfied.Yet, few tools exist to support this decision, especially when it affects reaction kinetics, and researchers are often left to choose on the basis of qualitative chemical knowledge and/or extensive and costly experimental investigations.Advances in the understanding of liquid-phase reactions remain a topic of intense academic interest 5 and practical relevance, as illustrated by identification of the development of solvent selection techniques as a key priority area by the ACS Green Chemistry roundtable. 6 A very promising avenue of research in this direction is the development of Computer-Aided Molecular Design (CAMD) techniques. CAMD offers systematic methodologies, typically in the form of algorithms, to identify chemical species/molecular structures that perform a chosen function best (e.g., maximize the rate of a given reaction). The resulting molecular designs can be used to guide experiments in an otherwise huge space of possibilities. CAMD techniques have been widely applied in the context of solvent design for separations, and have had a significant impact on academic and industrial practice. 7 In a batch extractive distillation for fine chemicals processing, for example, ...

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“…71,72 The issue of determining the best solvent for a specific chemical reaction has been recently addressed by combining quantum chemistry calculations with computeraided molecular design. 73 Thus, accurate prediction on solvent effects remain an important goal and can be very useful in the future studies of the design of best solvent for diverse chemical reactions of practical interest. 74 …”

Robust chemical product design via fuzzy optimisation approach

“…The application of SAFT-VR is later applied by Mac Dowell et al (2011) to describe the fluid phase behaviour in modelling of amines and mixtures of amines with water and carbon dioxide. Struebing et al (2013) proposed a systematic methodology that identifies improved reaction solvents by combining quantum mechanical computations of reaction rate constant with CAMD procedure. The developed methodology allows the identification of high performance solvent within a large set of possible molecules.…”

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

“…Discrete variables are used to represent molecularlevel decisions such as the number of groups of a given kind (for example, how many hydroxyl groups the optimal molecule contains, if any), with constraints used to specify how the groups can be combined. [6][7][8][9] Discrete variables can also be used to represent the connectivity between the groups, 10,11 and the identity of components if the material of interest is a mixture. [12][13][14] The CAMPD problem is inherently more complex than the corresponding process design problem with fixed material choices.…”