Mixture design using derivative‐free optimization in the space of individual component properties

Austin, Nick; Samudra, Apurva; Sahinidis, Nikolaos V.; Trahan, Daniel W.

doi:10.1002/aic.15142

Cited by 21 publications

(22 citation statements)

References 51 publications

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“…26 The CAMD method was then extended to the design of mixtures and composite chemical products, and identified as computer-aided blend design (CAM b D) 27 also reported as computeraided mixture design (CAM x D). 28,29 Typically, almost all CAMD/CAM b D methods use group contribution-based property prediction methods 30,31 to evaluate the generated compound with respect to the specified set of desirable target properties. UNIFAC 32,33 (universal quasichemical functional-group activity coefficients) and SAFT-γ 34 (statistical associating fluid theory) demonstrated to be accurate and useful in calculating solubility, phase equilibrium, partition coefficients, and various other properties.…”

Section: Theoretical Framework For Formulated Products Designmentioning

confidence: 99%

Automated robotic platforms in design and development of formulations

2021

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Product design for formulations is an active and challenging area of research. The new challenges of a fast-paced market, products of increasing complexity, and practical translation of sustainability paradigms require to reexamine the existing theoretical frameworks to include the advantages deriving from the new reality of digitalization of business and research. In this work, we review the existing approaches, clearly stating the role of automation and machine-learning-guided optimization in the broader framework. Moving from this, we review the state of the art of automated hardware and software for formulated product design, and identify the open challenges for future research. Perspectives are given on the emerging fields of automated discovery, scale-up, and multistage optimization, and a unitary picture of the existing connections is provided, in the general context of a completely digital R&D workflow.

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Section: Theoretical Framework For Formulated Products Designmentioning

confidence: 99%

Automated robotic platforms in design and development of formulations

2021

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“…Approaches similar in nature [82,4] have also optimized in the space of structures and evaluated each structure/mixture as an input to process design problems.…”

Section: Decomposition In Integrated Product/process Designmentioning

confidence: 99%

“…Karunanithi et al [82] developed a decomposition algorithm to address difficult optimization problems, applying it to the design of liquid-liquid extraction solvents and crystallization solvents [83]. Subsequent investigations of this problem came from Samudra and Sahinidis [147] and Austin et al [4].…”

Section: Single Molecule Designmentioning

confidence: 99%

“…Conte et al [33] proposed a task-based decomposition algorithm, which was applied to the design of paint solvents blends and insect repellent solvent blends. Austin et al [4] addressed the mixture design problem in the reduced-order space of each individual component's properties, employing derivative-free optimization methods to optimize over the lower-dimensional space. This was applied to reproduce the crystallization solvent design problems of Karunanithi et al [83].…”

Section: Mixture Designmentioning

confidence: 99%

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Computer-aided molecular design: An introduction and review of tools, applications, and solution techniques

Austin

Sahinidis

Trahan

2016

Chemical Engineering Research and Design

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171

117

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This article provides an introduction to and review of the field of computer-aided molecular design (CAMD). It is intended to be approachable for the absolute beginner as well as useful to the seasoned CAMD practitioner. We begin by discussing various quantitative structure-property relationships (QSPRs) which have been demonstrated to work well with CAMD problems. The methods discussed in this article are (1) group contribution methods, (2) topological indices, and (3) signature descriptors. Next, we present general optimization formulations for various forms of the CAMD problem. Common design constraints are discussed and structural feasibility constraints are provided for the three types of QSPRs addressed. We then detail useful techniques for approaching CAMD optimization problems, including decomposition methods, heuristic approaches, and mathematical programming strategies. Finally, we discuss many applications that have been addressed using CAMD.

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“…In this particular application, our problem space is defined by the 5‐dimensional σ moment vector of

[M_{0}, M_{2}, M_{3}, M_{don}, M_{acc}]

. From previous studies, we know that DFO solvers work very well on problems with few degrees of freedom and are efficient at solving CAMD/CAMxD problems …”

Section: The Cosmo‐based Solvent Design Problemmentioning

confidence: 99%

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

et al. 2017

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In this article, we investigate reaction solvent design using COSMO-RS thermodynamics in conjunction with computeraided 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 r-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.

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Mixture design using derivative‐free optimization in the space of individual component properties

Cited by 21 publications

References 51 publications

Automated robotic platforms in design and development of formulations

Automated robotic platforms in design and development of formulations

Computer-aided molecular design: An introduction and review of tools, applications, and solution techniques

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

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