This thesis explores mathematical optimization techniques to address the computeraided molecular and mixture design problems (CAMD/CAMxD). In particular, we leverage the power of mixed-integer linear programs (MILPs) to quickly and efficiently design over the massive chemical search space. These MILPs, when coupled with state-ofthe- art derivative-free optimization (DFO) methods, make for an efficient optimization strategy when designing mixtures of molecules or when considering a single molecule design problem that involves difficult thermodynamics or process models. In the first chapter, we provide a very general overview of the field of CAMD as addressed from the perspective of mathematical optimization. We discuss many relevant quantitative structure-property relationships (QSPRs) and provide constraints typically used in CAMD/CAMxD optimization problems. The second chapter introduces our DFO-based molecular/mixture design algorithm and describes how this approach enables a much greater molecular diversity to be considered in the search space as compared to traditional methods. Additionally, this chapter looks at a few case studies relevant to crystallization solvents and provides a detailed comparison of 27 different DFO algorithms for solving these problems. The third chapter introduces COSMO-RS/-SAC as alternatives to UNIFAC as the method used to capture mixture thermodynamics for a variety of CAMD/CAMxD problems. To fully incorporate COSMO-RS/-SAC into CAMD, we introduce group contribution (GC) methods for estimating a few necessary parameters for COSMO-based methods. We demonstrate the utility of COSMO-RS/-SAC in a few case studies for which UNIFAC-like methods are insufficient. In the fourth chapter, we investigate reaction solvent design using COSMO-based methods. COSMO-RS is particularly suitable for these problems as they allow for modeling of many relevant species in chemical reactions (transition states, charges, etc.) directly at the quantum level. This information can be immediately passed to the CAMD problem. We investigate a number of solvent design problems for a few difficult reactions. We summarize the work and provide a few future directions in the final chapter. Overall, this thesis serves to push the field of CAMD forward by introducing new methods to more efficiently explore the massive chemical search space and to enable a few new classes of problems which were previously untenable.

This work presents heuristics for searching large sets of molecular structures for low-energy, stable systems. The goal is to find the globally optimal structures in less time or by consuming less computational resources. The strategies intermittently evaluate and rank structures during molecular dynamics optimizations, culling possible weaker solutions from evaluations earlier, leaving better solutions to receive more simulation time. Although some imprecision was introduced from not allowing all structures to fully optimize before ranking, the strategies identify metrics that can be used to make these searches more efficient when computational resources are limited.

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Comprehensive molecular design was used to identify new heat transfer fluids for direct immersion phase change cooling of electronic systems. Four group contribution methods for thermophysical properties relevant to heat transfer were critically evaluated and new group contributions were regressed for organosilicon compounds. 52 new heat transfer fluids were identified via computer-aided molecular design and figure of merit analysis. Among these 52 fluids, 9 fluids were selected for experimental evaluation and their thermophysical properties were experimentally measured to validate the group contribution estimates. Two of the 9 fluids (C6H11F3 and C5H6F6O) were synthesized in this work. Pool boiling experiments showed that the new fluids identified in this work have superior heat transfer properties than existing coolant HFE 7200. The radiative forcing and global warming potential of new fluids, calculated via a new group contribution method developed in this work and FT-IR analysis, were found to be significantly lower than those of current coolants. The approach of increasing the thermal conductivity of heat transfer fluids by dispersing nanoparticles was also investigated. A model for the thermal conductivity of nanoparticle dispersions (nanofluids) was developed that incorporates the effect of size on the intrinsic thermal conductivity of nanoparticles. The model was successfully applied to a variety of nanoparticle-fluid systems. Rheological properties of nanofluids were also investigated and it was concluded that the addition of nanoparticles to heat transfer fluids may not be beneficial for electronics cooling due to significantly larger increase in viscosity relative to increase in thermal conductivity.

Dans un contexte de prise en compte croissante des enjeux environnementaux, l'industrie de la chimie et des procédés se retrouve confrontée à des problématiques de substitution de molécules. Les méthodes de formulation inverse, qui consistent en la recherche assistée par ordinateur de molécules satisfaisant un ensemble de contraintes, répondent de manière efficace à ces problématiques. A partir de l'analyse systémique des usages et fonctionnalités nécessaires dans ce contexte, nous développons un outil logiciel de formulation inverse mettant en oeuvre un algorithme génétique. Celui-ci est capable d'explorer un espace de solutions plus vaste en considérant les mélanges et non les molécules seules. Par ailleurs, il propose une définition des problèmes très flexible qui permet la recherche efficiente de molécules issues de filières renouvelables. En s'appuyant sur l'ingénierie système et l'ingénierie d'entreprise, nous proposons un processus formel de prise de décision pour la substitution de produit dans un contexte industriel. Ce processus de décision multi-critères englobe les phases de définition des exigences, de génération de solutions alternatives, de sélection de la meilleure alternative et de mise en oeuvre du produit. Il utilise une approche dirigée par les modèles et des techniques de prises de décision qui garantissent un alignement opérationnel en complément de l'alignement stratégique. A travers un cas d'étude, nous montrons comment l'utilisation conjointe de notre outil de recherche par formulation inverse et de notre processus de décision permet une démarche environnementale de substitution de produit à la fois efficiente et conforme à la réalité de l'entreprise. / In a context where environmental issues are increasingly taken into account, the chemical related industry faces situations imposing a chemical product substitution. Computer aided molecular design methods, which consist in finding molecules satisfying a set of constraints, are well adapted to these situations. Using a systemic analysis of the needs and uses linked to this context, we develop a computer aided product design tool implementing a genetic algorithm. It is able to explore a wider solution space thanks to a flexible molecular framework. Besides, by allowing a very flexible setting of the problem to be solved, it enables the search of molecules sourced from renewable resources. Based on concepts from system and enterprise engineering, we formalize a decision making process dedicated to the product substitution in an industrial context. This multi-criteria decision process includes the phases of the requirements definition, of the generation of alternative solutions, of the selection of the best alternative and of the product application. It uses a model driven approach and decision making techniques that guaranty an operational alignment in addition to the strategic alignment across the chemical enterprise. Through a case study, we expose how the combination of our computer aided product design tool and our decision making process enables an environmentally compliant approach of product substitution which is both efficient and in adequacy with enterprise context.