Ab initio structure determination of n-diamond

Li, Da; Tian, Fubo; Chu, Binhua; Duan, Defang; Sha, Xiaojing; Lv, Yunzhou; Zhang, Huadi; Lü, Nan; Liu, Bingbing; Cui, Tian

doi:10.1038/srep13447

Cited by 18 publications

(15 citation statements)

References 59 publications

(101 reference statements)

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“…In order to investigate the diamond-polymorph issue, we selected several synthetic and natural samples for study. The synthetic samples were produced through chemical vapor deposition (CVD), and the natural ones were obtained from primitive (Murchison and Orgueil) and impact-shocked (Gujba) meteorites 42 . Samples of similar origins reportedly contain various diamond polymorphs 9 10 11 12 13 16 25 27 .…”

Section: Resultsmentioning

confidence: 99%

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Twinning of cubic diamond explains reported nanodiamond polymorphs

Németh

Garvie

Buseck

2015

Sci Rep

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The unusual physical properties and formation conditions attributed to h-, i-, m-, and n-nanodiamond polymorphs has resulted in their receiving much attention in the materials and planetary science literature. Their identification is based on diffraction features that are absent in ordinary cubic (c-) diamond (space group: Fd-3m). We show, using ultra-high-resolution transmission electron microscope (HRTEM) images of natural and synthetic nanodiamonds, that the diffraction features attributed to the reported polymorphs are consistent with c-diamond containing abundant defects. Combinations of {113} reflection and <011> rotation twins produce HRTEM images and d-spacings that match those attributed to h-, i-, and m-diamond. The diagnostic features of n-diamond in TEM images can arise from thickness effects of c-diamonds. Our data and interpretations strongly suggest that the reported nanodiamond polymorphs are in fact twinned c-diamond. We also report a new type of twin (<11> rotational), which can give rise to grains with dodecagonal symmetry. Our results show that twins are widespread in diamond nanocrystals. A high density of twins could strongly influence their applications.

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Section: Resultsmentioning

confidence: 99%

“…Recently, Li et al . 42 suggested a structure model for n-diamond consisting of layers of h-diamond and C 3 isosceles triangle rings. In any case, there is no consensus on the structure or synthesis conditions of the polymorphs.…”

mentioning

confidence: 99%

Twinning of cubic diamond explains reported nanodiamond polymorphs

Németh

Garvie

Buseck

2015

Sci Rep

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“…New diamond (n-diamond) was proposed as a new carbon allotrope; its electron diffraction pattern matches that of cubic (Fd-3m) diamond apart from some additional reflections that are forbidden for diamond, indexed as {200}, {222} and {420}. The speculation of this new allotrope was first reported in 1991 [13], but the exact crystal structure of n-diamond has remained as a controversy despite several attempts to explain the n-diamond diffraction pattern [86][87][88][89]. Here, we attempt to explain the crystal structure of the metastable n-diamond using our metastable phase diagrams.…”

Section: Sjmentioning

confidence: 99%

Machine Learning the Metastable Phase Diagram of Materials

Srinivasan

Batra

Luo

et al. 2022

Preprint

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A central feature of materials synthesis is the concept of phase diagrams, which provide information on the phases of the material at any given thermodynamic condition (i.e., state variables such as pressure, temperature and composition). Conventional phase diagram generation involves experimentation to provide an initial estimate of the set of thermodynamically accessible phases and their boundaries, followed by use of phenomenological models to interpolate between the available experimental data points and extrapolate to experimentally inaccessible regions. Such an approach, combined with high throughput first-principles calculations and data-mining techniques, has led to exhaustive thermodynamic databases (e.g. compatible with the CALPHAD method), albeit focused on the reduced set of phases observed at distinct thermodynamic equilibria. In contrast, materials during their synthesis, operation, or processing, may not reach their thermodynamic equilibrium state but, instead, remain trapped in a local (metastable) free energy minimum, which may exhibit desirable properties. A phase diagram that maps these metastable phases and their thermodynamic behavior is highly desirable but currently lacking, due to the vast configurational landscape. Here, we introduce an automated workflow that integrates first-principles physics and atomistic simulations with machine learning (ML), and high-performance computing to allow rapid exploration of the metastable phases of a given elemental composition and construct "metastable" phase diagrams for materials far-from-equilibrium. Using carbon, a prototypical system with a large number of metastable phases without parent in equilibrium, we demonstrate automated metastable phase diagram construction to map hundreds of metastable states ranging from near equilibrium to those far-from-equilibrium (400 meV/atom). Moreover, we incorporate the free energy calculations into a neural-network-based learning of the equations of state that allows for efficient construction of metastable phase diagrams. We use the metastable phase diagram and identify domains of relative stability and synthesizability of metastable materials. High temperature high pressure experiments using a diamond anvil cell on graphite sample coupled with high-resolution transmission electron microscopy (HRTEM) confirm our metastable phase predictions. In particular, we identify the previously ambiguous structure of n-diamond as a cubic-analog of diaphite-like lonsdaelite phase. The workflow presented here is general and broadly applicable to other chemical systems.

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“…New diamond (n-diamond) was proposed as a new carbon allotrope; its electron diffraction pattern matches that of cubic (Fd-3m) diamond apart from some additional reflections that are forbidden for diamond, indexed as {200}, {222} and {420}. The speculation of this new allotrope was first reported in 1991 [13], but the exact crystal structure of n-diamond has remained as a controversy despite several attempts to explain the n-diamond diffraction pattern [83][84][85][86]. Here, we attempt to explain the crystal structure of the metastable n-diamond using our metastable phase diagrams.…”

Section: High-temperature High-pressure Experimentsmentioning

confidence: 99%

Machine Learning the Metastable Phase Diagram of Materials

Srinivasan

Batra

Luo

et al. 2021

Preprint

View full text Add to dashboard Cite

A central feature of materials synthesis is the concept of phase diagrams. Phase diagrams are an invaluable tool for material synthesis and provide information on the phases of the material at any given thermodynamic condition (i.e., state variables such as pressure, temperature and composition). Conventional phase diagram generation involves experimentation to provide an initial estimate of the set of thermodynamically accessible phases and their boundaries, followed by use of phenomenological models to interpolate between the available experimental data points and extrapolate to experimentally inaccessible regions. Such an approach, combined with high throughput first-principles calculations and data-mining techniques, has led to exhaustive thermodynamic databases (e.g. compatible with the CALPHAD method), albeit focused on the reduced set of phases observed at distinct thermodynamic equilibria. In contrast, materials during their synthesis, operation, or processing, may not reach their thermodynamic equilibrium state but, instead, remain trapped in a local (metastable) free energy minimum, that may exhibit desirable properties. A phase diagram that maps these metastable phases and their thermodynamic behavior is highly desirable but currently lacking, due to the vast configurational landscape. Here, we introduce an automated workflow that integrates first-principles physics and atomistic simulations with machine learning (ML), and high-performance computing to allow rapid exploration of the metastable phases of a given elemental composition and construct "metastable" phase diagrams for materials far-from-equilibrium. Using carbon, a prototypical system with a vast number of metastable phases without parent in equilibrium, we demonstrate automated metastable phase diagram construction to map hundreds of metastable states ranging from near equilibrium to those far-from-equilibrium (400 meV/atom). Moreover, we incorporate the free energy calculations into a neural-network-based learning of the equations of state that allows for efficient construction of metastable phase diagrams. We use the metastable phase diagram and identify domains of relative stability and synthesizability of metastable materials. High temperature high pressure experiments using a diamond anvil cell on graphite sample coupled with high-resolution transmission electron microscopy (HRTEM) confirm our metastable phase predictions. The workflow presented here is general and broadly applicable to single and multi-component systems.

show abstract

Ab initio structure determination of n-diamond

Cited by 18 publications

References 59 publications

Twinning of cubic diamond explains reported nanodiamond polymorphs

Twinning of cubic diamond explains reported nanodiamond polymorphs

Machine Learning the Metastable Phase Diagram of Materials

Machine Learning the Metastable Phase Diagram of Materials

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