Machine Learning for Organic Cage Property Prediction

Turcani, Lukas; Greenaway, Rebecca L.; Jelfs, Kim E.

doi:10.1021/acs.chemmater.8b03572

Cited by 62 publications

(92 citation statements)

References 41 publications

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“…This webtool utilizes a specific polymer repeat unit fingerprinting method to estimate the properties of any polymer, with certain limitations (e.g., certain types of ladder polymers are currently incompatible with the webtool). Even though the current version works only on linear polymers (and can predict solvent‐polymer interactions), incorporating solvent–cage interactions is a clear research need in the area of MMCM formation –…”

Section: Challengesmentioning

confidence: 99%

Molecularly Mixed Composite Membranes: Challenges and Opportunities

Zhu

O’Nolan

Lively

2019

Chemistry A European J

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The fabrication of porous molecules, such as metal–organic polyhedra (MOPs), porous organic cages (POCs) and others, has given rise to the potential for creating “solid solutions” of molecular fillers and polymers. Such solid solutions circumvent longstanding interface issues associated with mixed matrix membranes (MMMs), and are referred to as molecularly mixed composite membranes (MMCMs) to distinguish them from traditional two‐phase MMMs. Early investigations of MMCMs highlight the advantages of solid solutions over MMMs, including dispersion of the filler, anti‐plasticization of the polymer network, and removal of deleterious interfacial issues. However, the exact microscopic structure as well as the transport modality in this new class of membrane are not well understood. Moreover, there are clear engineering challenges that need to be addressed for MMCMs to transition into the field. In this Minireview, the authors outline several scientific and technological challenges associated with the aforementioned questions and their suggestions to tackle them.

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Section: Challengesmentioning

confidence: 99%

Molecularly Mixed Composite Membranes: Challenges and Opportunities

Zhu

O’Nolan

Lively

2019

Chemistry A European J

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“…There has been much progress in the first use-case through the development of quantitative structure activity relationship (QSAR) models using deep learning [Ma et al, 2015]. These models have achieved state-of-the-art results in predicting properties Ryu et al [2018a,b], Turcani et al [2018], Dey et al [2018], , Gu et al [2019], Zeng et al [2018], Coley et al [2019a] as well as property uncertainties Cortés-Ciriano and Bender [2018], Zhang and Lee [2019], Janet et al [2019], Ryu et al [2019] of known molecules.…”

Section: Introductionmentioning

confidence: 99%

Constrained Bayesian optimization for automatic chemical design using variational autoencoders

2020

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Automatic Chemical Design is a framework for generating novel molecules with optimized properties. The original scheme, featuring Bayesian optimization over the latent space of a variational autoencoder, suffers from the pathology that it tends to produce invalid molecular structures. First, we demonstrate empirically that this pathology arises when the Bayesian optimization scheme queries latent points far away from the data on which the variational autoencoder has been trained. Secondly, by reformulating the search procedure as a constrained Bayesian optimization problem, we show that the effects of this pathology can be mitigated, yielding marked improvements in the validity of the generated molecules. We posit that constrained Bayesian optimization is a good approach for solving this class of training set mismatch in many generative tasks involving Bayesian optimization over the latent space of a variational autoencoder.

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“…We provided a web-based tool that allows for easy prediction of shape persistence of a system to an experimental chemist, prior to any attempted laboratory synthesis. [55]…”

Section: Kim Jelfs Is a Senior Lecturer And Royal Societymentioning

confidence: 99%

High‐Throughput Approaches for the Discovery of Supramolecular Organic Cages

Greenaway

Jelfs

2020

ChemPlusChem

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The assembly of complex molecules, such as organic cages, can be achieved through supramolecular and dynamic covalent strategies. Their use in a range of applications has been demonstrated, including gas uptake, molecular separations, and in catalysis. However, the targeted design and synthesis of new species for particular applications is challenging, particularly as the systems become more complex. High‐throughput computation‐only and experiment‐only approaches have been developed to streamline the discovery process, although are still not widely implemented. Additionally, combined hybrid workflows can dramatically accelerate the discovery process and lead to the serendipitous discovery and rationalisation of new supramolecular assemblies that would not have been designed based on intuition alone. This Minireview focuses on the advances in high‐throughput approaches that have been developed and applied in the discovery of supramolecular organic cages.

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Machine Learning for Organic Cage Property Prediction

Cited by 62 publications

References 41 publications

Molecularly Mixed Composite Membranes: Challenges and Opportunities

Molecularly Mixed Composite Membranes: Challenges and Opportunities

Constrained Bayesian optimization for automatic chemical design using variational autoencoders

High‐Throughput Approaches for the Discovery of Supramolecular Organic Cages

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