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

scholarly journals Unusual Dependence of the Diamond Growth Rate on the Methane Concentration in the Hot Filament Chemical Vapor Deposition Process

Materials ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 426
Author(s):  
Byeong-Kwan Song ◽  
Hwan-Young Kim ◽  
Kun-Su Kim ◽  
Jeong-Woo Yang ◽  
Nong-Moon Hwang
Keyword(s):  
Growth Rate ◽  
Chemical Vapor Deposition ◽  
Vapor Deposition ◽  
Methane Concentration ◽  
Chemical Vapor ◽  
Diamond Growth ◽  
Lower Growth ◽  
Filament Temperature ◽  
Diamond Phase ◽  
Hot Filament

Although the growth rate of diamond increased with increasing methane concentration at the filament temperature of 2100 °C during a hot filament chemical vapor deposition (HFCVD), it decreased with increasing methane concentration from 1% CH4 –99% H2 to 3% CH4 –97% H2 at 1900 °C. We investigated this unusual dependence of the growth rate on the methane concentration, which might give insight into the growth mechanism of a diamond. One possibility would be that the high methane concentration increases the non-diamond phase, which is then etched faster by atomic hydrogen, resulting in a decrease in the growth rate with increasing methane concentration. At 3% CH4 –97% H2, the graphite was coated on the hot filament both at 1900 °C and 2100 °C. The graphite coating on the filament decreased the number of electrons emitted from the hot filament. The electron emission at 3% CH4 –97% H2 was 13 times less than that at 1% CH4 –99% H2 at the filament temperature of 1900 °C. The lower number of electrons at 3% CH4 –97% H2 was attributed to the formation of the non-diamond phase, which etched faster than diamond, resulting in a lower growth rate.

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.

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