Computational Discovery of New Zeolite-Like Materials

2009 ◽  
Vol 113 (51) ◽  
pp. 21353-21360 ◽  
Author(s):  
Michael W. Deem ◽  
Ramdas Pophale ◽  
Phillip A. Cheeseman ◽  
David J. Earl
Keyword(s):  
Computational Discovery

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>

scholarly journals Formation of a single quasicrystal upon collision of multiple grains

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Insung Han ◽  
Kelly L. Wang ◽  
Andrew T. Cadotte ◽  
Zhucong Xi ◽  
Hadi Parsamehr ◽  
...  
Keyword(s):  
Grain Boundaries ◽  
Large Scale ◽  
Thin Film Deposition ◽  
Film Deposition ◽  
Decagonal Quasicrystal ◽  
Bulk Crystal Growth ◽  
Computational Discovery ◽  
Dynamics Simulations ◽  
Novel Applications ◽  
Small Misorientation

AbstractQuasicrystals exhibit long-range order but lack translational symmetry. When grown as single crystals, they possess distinctive and unusual properties owing to the absence of grain boundaries. Unfortunately, conventional methods such as bulk crystal growth or thin film deposition only allow us to synthesize either polycrystalline quasicrystals or quasicrystals that are at most a few centimeters in size. Here, we reveal through real-time and 3D imaging the formation of a single decagonal quasicrystal arising from a hard collision between multiple growing quasicrystals in an Al-Co-Ni liquid. Through corresponding molecular dynamics simulations, we examine the underlying kinetics of quasicrystal coalescence and investigate the effects of initial misorientation between the growing quasicrystalline grains on the formation of grain boundaries. At small misorientation, coalescence occurs following rigid rotation that is facilitated by phasons. Our joint experimental-computational discovery paves the way toward fabrication of single, large-scale quasicrystals for novel applications.

scholarly journals Computational discovery of a new rhombohedral diamond phase

2018 ◽  
Vol 98 (9) ◽  
Author(s):  
Zhen-Zhen Li ◽  
Jian-Tao Wang ◽  
Hiroshi Mizuseki ◽  
Changfeng Chen
Keyword(s):  
Diamond Phase ◽  
Computational Discovery

Computational Discovery of SARS-CoV-2 NSP 16 Drug Candidates Based on Pharmacophore Modeling and Molecular Dynamics Simulation

2021 ◽  
Vol 20 (04) ◽  
pp. 377-390
Author(s):  
Zahra Hesari ◽  
Samaneh Zolghadri ◽  
Sajad Moradi ◽  
Mohsen Shahlaei ◽  
Elham Tazikeh-Lemeski
Keyword(s):  
Molecular Dynamics ◽  
Study Drug ◽  
Pharmacophore Modeling ◽  
Binding Energies ◽  
Dynamics Simulation ◽  
Structural Protein ◽  
Drug Candidates ◽  
Computational Discovery ◽  
Approved Drugs ◽  
Dynamics Simulations

Non-Structural Protein 16 (NSP-16) is one of the most suitable targets for discovery of drugs for corona viruses including SARS-CoV-2. In this study, drug discovery of SARS-CoV-2 nsp-16 has been accomplished by pharmacophore-based virtual screening among some analogs (FDA approved drugs) and marine natural plants (MNP). The comparison of the binding energies and the inhibition constants was determined using molecular docking method. Three compounds including two FDA approved (Ibrutinib, Idelalisib) and one MNP (Kumusine) were selected for further investigation using the molecular dynamics simulations. The results indicated that Ibrutinib and Idelalisib are oral medications while Kumusine, with proper hydrophilic and solubility properties, is an appropriate candidate for nsp-16 inhibitor and can be effective to control COVID-19 disease.

Chapter 13. Computational Discovery of Organic LED Materials

2018 ◽  
pp. 423-446
Author(s):  
Rafael Gómez-Bombarelli ◽  
Alán Aspuru-Guzik
Keyword(s):  
Computational Discovery

Computational Discovery of Stable Heteroanionic Oxychalcogenides ABXO (A, B = Metals; X = S, Se, and Te) and Their Potential Applications

2020 ◽  
Vol 32 (19) ◽  
pp. 8229-8242
Author(s):  
Jiangang He ◽  
Zhenpeng Yao ◽  
Vinay I. Hegde ◽  
S. Shahab Naghavi ◽  
Jiahong Shen ◽  
...  
Keyword(s):  
Potential Applications ◽  
Computational Discovery
Sign in / Sign up

Export Citation Format

Share Document