Atomistic Evidence of Nucleation Mechanism for the Direct Graphite-to-Diamond Transformation

Luo, Duan; Yang, Liuxiang; Xie, Hongxian; Srinivasan, Srilok; Tian, Jinshou; Sankaranarayanan, Subramanian K. R. S.; Arslan, Ilke; Yang, Wenge; Mao, Ho‐kwang; Wen, Jianguo

doi:10.21203/rs.3.rs-1091313/v1

Cited by 2 publications

(3 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Such anisotropic pressure can be explained by the buckling of basal planes, which induce collapse of c axis, equivalent to a huge increase in pressure in the out-of-plane direction. In fact, many rhombus voids within a single crystal, resulting from anisotropic pressure differentials, have been reported in diamond anvil experiments [70]. It is predicted that n-diamond nucleates at these bent areas [90].…”

Section: Sjmentioning

confidence: 98%

“…We use the information derived from the metastable phase diagram to explain the experimental observations during laser heating induced phase transformation of hexagonal graphite in a pressurized diamond anvil cell [70,71]. As described in Ref [71] and Ref [70], the graphite crystal was heated to ≈1400 K by a YAG laser at the center of crystal. Due to Gaussian distribution of laser spot, a temperature gradient exists from the center to outside within a single laser spot in a given sample.…”

Section: Sjmentioning

confidence: 99%

“…In these recovered samples with incomplete conversion of diamond, several metastable phases were identified by HRTEM as shown in Figure 3. When pressurized, the graphite layers slide with respect to each other to form orthorhombic and rhombohedral graphite (Figure 3(a)) [70,[74][75][76]. With further increase in temperature, the orthorhombic and rhombohedral graphite layers buckle to form interlayer bonds resulting in the formation of hexagonal or cubic diamond respectively [74,75,[77][78][79][80][81][82][83].…”

Section: Sjmentioning

confidence: 99%

See 2 more Smart Citations

Machine Learning the Metastable Phase Diagram of Materials

Srinivasan

Batra

Luo

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Sjmentioning

confidence: 98%

Section: Sjmentioning

confidence: 99%

Section: Sjmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning the Metastable Phase Diagram of Materials

Srinivasan

Batra

Luo

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

Machine learning the metastable phase diagram of covalently bonded carbon

et al. 2022

Self Cite

View full text Add to dashboard Cite

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. 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 to construct “metastable” phase diagrams for materials far-from-equilibrium. Using carbon as a prototypical system, we demonstrate automated metastable phase diagram construction to map hundreds of metastable states ranging from near equilibrium to far-from-equilibrium (400 meV/atom). 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.

show abstract

Atomistic Evidence of Nucleation Mechanism for the Direct Graphite-to-Diamond Transformation

Cited by 2 publications

References 45 publications

Machine Learning the Metastable Phase Diagram of Materials

Machine Learning the Metastable Phase Diagram of Materials

Machine learning the metastable phase diagram of covalently bonded carbon

Contact Info

Product

Resources

About