Computational Screening of Porous Organic Molecules for Xenon/Krypton Separation

2017 ◽  
Vol 121 (28) ◽  
pp. 15211-15222 ◽  
Author(s):  
Marcin Miklitz ◽  
Shan Jiang ◽  
Rob Clowes ◽  
Michael E. Briggs ◽  
Andrew I. Cooper ◽  
...  
Keyword(s):  
Organic Molecules ◽  
Computational Screening

scholarly journals Materials Precursor Score: Modelling Chemists' Intuition for the Synthetic Accessibility of Porous Organic Cages

2021 ◽  
Author(s):  
Steven Bennett ◽  
Filip Szczypiński ◽  
Lukas Turcani ◽  
Michael Briggs ◽  
Rebecca L. Greenaway ◽  
...  
Keyword(s):  
Organic Molecules ◽  
Class Imbalance ◽  
Computational Prediction ◽  
Material Development ◽  
Computational Screening ◽  
Machine Learning Model ◽  
Synthetic Accessibility ◽  
Internal Cavities ◽  
Computational Discovery ◽  
Novel Structures

<div>Computation is increasingly being used to try to accelerate the discovery of new materials. One specific example of this is porous molecular materials, specifically porous organic cages, where the porosity of the materials predominantly comes from the internal cavities of the molecules themselves. The computational discovery of novel structures with useful properties is currently hindered by the difficulty in transitioning from a computational prediction to synthetic realisation. Attempts at experimental validation are often time-consuming, expensive and, frequently, the key bottleneck of material discovery. In this work, we developed a computational screening workflow for porous molecules that includes consideration of the synthetic difficulty of material precursors, aimed at easing the transition between computational prediction and experimental realisation. We trained a machine learning model by first collecting data on 12,553 molecules categorised either as `easy-to-synthesise' or `difficult-to-synthesise' by expert chemists with years of experience in organic synthesis. We used an approach to address the class imbalance present in our dataset, producing a binary classifier able to categorise easy-to-synthesise molecules with few false positives. We then used our model during computational screening for porous organic molecules to bias towards precursors whose easier synthesis requirements would make them promising candidates for experimental realisation and material development. We found that even by limiting precursors to those that are easier-to-synthesise, we are still able to identify cages with favourable, and even some rare, properties. </div>

Experimental Validation of a Computational Screening Approach to Predict Redox Potentials for a Diverse Variety of Redox-Active Organic Molecules

2020 ◽  
Vol 124 (44) ◽  
pp. 24105-24114
Author(s):  
Alexandra R. McNeill ◽  
Samantha E. Bodman ◽  
Amy M. Burney ◽  
Chris D. Hughes ◽  
Deborah L. Crittenden
Keyword(s):  
Experimental Validation ◽  
Organic Molecules ◽  
Redox Potentials ◽  
Computational Screening ◽  
Screening Approach ◽  
Redox Active

scholarly journals Materials Precursor Score: Modelling Chemists' Intuition for the Synthetic Accessibility of Porous Organic Cages

2021 ◽  
Author(s):  
Steven Bennett ◽  
Filip Szczypiński ◽  
Lukas Turcani ◽  
Michael Briggs ◽  
Rebecca L. Greenaway ◽  
...  
Keyword(s):  
Organic Molecules ◽  
Class Imbalance ◽  
Computational Prediction ◽  
Material Development ◽  
Computational Screening ◽  
Machine Learning Model ◽  
Synthetic Accessibility ◽  
Internal Cavities ◽  
Computational Discovery ◽  
Novel Structures

<div>Computation is increasingly being used to try to accelerate the discovery of new materials. One specific example of this is porous molecular materials, specifically porous organic cages, where the porosity of the materials predominantly comes from the internal cavities of the molecules themselves. The computational discovery of novel structures with useful properties is currently hindered by the difficulty in transitioning from a computational prediction to synthetic realisation. Attempts at experimental validation are often time-consuming, expensive and, frequently, the key bottleneck of material discovery. In this work, we developed a computational screening workflow for porous molecules that includes consideration of the synthetic difficulty of material precursors, aimed at easing the transition between computational prediction and experimental realisation. We trained a machine learning model by first collecting data on 12,553 molecules categorised either as `easy-to-synthesise' or `difficult-to-synthesise' by expert chemists with years of experience in organic synthesis. We used an approach to address the class imbalance present in our dataset, producing a binary classifier able to categorise easy-to-synthesise molecules with few false positives. We then used our model during computational screening for porous organic molecules to bias towards precursors whose easier synthesis requirements would make them promising candidates for experimental realisation and material development. We found that even by limiting precursors to those that are easier-to-synthesise, we are still able to identify cages with favourable, and even some rare, properties. </div>

High Resolution Replication of Organoclay Surfaces

1982 ◽  
Vol 40 ◽  
pp. 576-577
Author(s):  
W. W. Barker ◽  
W. E. Rigsby ◽  
V. J. Hurst ◽  
W. J. Humphreys
Keyword(s):  
Clay Mineral ◽  
Organic Molecule ◽  
Organic Molecules ◽  
X Ray Diffraction ◽  
Xrd Pattern ◽  
X Ray ◽  
Naturally Occurring ◽  
Silicate Layers ◽  
Mineral Identification

Experimental clay mineral-organic molecule complexes long have been known and some of them have been extensively studied by X-ray diffraction methods. The organic molecules are adsorbed onto the surfaces of the clay minerals, or intercalated between the silicate layers. Natural organo-clays also are widely recognized but generally have not been well characterized. Widely used techniques for clay mineral identification involve treatment of the sample with H2 O2 or other oxidant to destroy any associated organics. This generally simplifies and intensifies the XRD pattern of the clay residue, but helps little with the characterization of the original organoclay. Adequate techniques for the direct observation of synthetic and naturally occurring organoclays are yet to be developed.

Direct phase determination in electron crystallography: small organic molecules

1992 ◽  
Vol 50 (2) ◽  
pp. 1166-1167
Author(s):  
Douglas L. Dorset
Keyword(s):  
Organic Molecules ◽  
Data Sets ◽  
Temperature Structure ◽  
3D Analysis ◽  
Intensity Data ◽  
Electron Crystallography ◽  
Phase Determination ◽  
Measured Intensity ◽  
3D Data

The quantitative use of electron diffraction intensity data for the determination of crystal structures represents the pioneering achievement in the electron crystallography of organic molecules, an effort largely begun by B. K. Vainshtein and his co-workers. However, despite numerous representative structure analyses yielding results consistent with X-ray determination, this entire effort was viewed with considerable mistrust by many crystallographers. This was no doubt due to the rather high crystallographic R-factors reported for some structures and, more importantly, the failure to convince many skeptics that the measured intensity data were adequate for ab initio structure determinations.We have recently demonstrated the utility of these data sets for structure analyses by direct phase determination based on the probabilistic estimate of three- and four-phase structure invariant sums. Examples include the structure of diketopiperazine using Vainshtein's 3D data, a similar 3D analysis of the room temperature structure of thiourea, and a zonal determination of the urea structure, the latter also based on data collected by the Moscow group.

A MOLECULAR DYNAMICS STUDY OF ELECTRONIC SPUTTERING OF LARGE ORGANIC MOLECULES

1989 ◽  
Vol 50 (C2) ◽  
pp. C2-33-C2-35 ◽  
Author(s):  
D. FENYÖ ◽  
B. U.R. SUNDQVIST ◽  
B. KARLSSON ◽  
R. E. JOHNSON
Keyword(s):  
Molecular Dynamics ◽  
Organic Molecules
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