Carbon under pressure

Sundqvist, Bertil

doi:10.1016/j.physrep.2020.12.007

Cited by 85 publications

(59 citation statements)

References 662 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This growth of rot over a range of temperatures is qualitatively in agreement with the existence of a range of temperatures over which C 60 transitions from fully orientationally ordered to fully orientationally disordered. Within that range, from ∼ 90 K to 260 K, certain fractions of molecules attain one of two possible specific relative orientations (so-called "P orientation" and "H orientation"), the precise fraction depending on and [46]. Again, we find in Fig.…”

Section: Solid Phase At Ambient Conditionsmentioning

confidence: 53%

“…Above the transition temperature, they are free to rotate about the fcc lattice sites. The specific orientation at low temperature can be retrieved by a concerted rotation of all four [43,46]. In that figure we show the potential energy surface as a function of .…”

Section: Solid Phase At Ambient Conditionsmentioning

confidence: 98%

“…C 60 has been extensively studied in the literature [41][42][43][44], see, e.g., Refs. [45] and [46] for recent overviews. There is fundamental interest, since C 60 is the molecule with the highest symmetry: 120 symmetry operations altogether (each an element in the icosahedral symmetry group), including 60 rotational symmetry operations.…”

Section: Structural Transitions In C 60mentioning

confidence: 99%

See 2 more Smart Citations

Machine learning force fields based on local parametrization of dispersion interactions: Application to the phase diagram of C60

et al. 2021

View full text Add to dashboard Cite

We present a comprehensive methodology to enable addition of van der Waals (vdW) corrections to machine learning (ML) atomistic force fields. Using a Gaussian approximation potential (GAP) [Bartók et al.., Phys. Rev. Lett. 104, 136403 (2010)] as baseline, we accurately machine learn a local model of atomic polarizabilities based on Hirshfeld volume partitioning of the charge density [Tkatchenko and Scheffler, Phys. Rev. Lett. 102, 073005 ( 2009)]. These environment-dependent polarizabilities are then used to parametrize a screened Londondispersion approximation to the vdW interactions. Our ML vdW model only needs to learn the charge density partitioning implicitly, by learning the reference Hirshfeld volumes from density functional theory (DFT). In practice, we can predict accurate Hirshfeld volumes from the knowledge of the local atomic environment (atomic positions) alone, making the model highly computationally efficient. For additional efficiency, our ML model of atomic polarizabilities reuses the same many-body atomic descriptors used for the underlying GAP learning of bonded interatomic interactions. We also show how the method enables straightforward computation of gradients of the observables, even when these remain challenging for the reference method (e.g., calculating gradients of the Hirshfeld volumes in DFT). Finally, we demonstrate the approach by studying the phase diagram of C 60 , where vdW effects are important. The need for a highly accurate vdW-inclusive reactive force field is highlighted by modeling the decomposition of the C 60 molecules taking place at high pressures and temperatures.

show abstract

Section: Solid Phase At Ambient Conditionsmentioning

confidence: 53%

Section: Solid Phase At Ambient Conditionsmentioning

confidence: 98%

Section: Structural Transitions In C 60mentioning

confidence: 99%

See 1 more Smart Citation

Machine learning force fields based on local parametrization of dispersion interactions: Application to the phase diagram of C60

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Pressure application on such supported systems appears as an interesting way to continuously tune these interactions and better understand them [6,7]. Graphene or graphene-based systems are particularly interesting in this regard due to their simple monoatomic nature and their relative ease of manipulation.…”

Section: Introductionmentioning

confidence: 99%

Delamination of multilayer graphene stacks from its substrate through wrinkle formation under high pressures

Amaral

Forestier

Piednoir

et al. 2021

Carbon

View full text Add to dashboard Cite

There is a need to find new paths for van der Waals 2D-systems detachment and transfer or to control their adhesion state in different environments. We have observed that supported multilayer graphene immersed in a fluid can be detached from a substrate through pressure application. The process is based on the development of wrinkles originated by the difference of in-plane-compressibility between the graphene stacks and the substrate. Graphene stacks comprised between 9 and 110 layers and immersed in various fluids allowed to investigate the growth and evolution of wrinkles with increasing pressure. The detachment from the substrate stops at the pressure-induced fluid solidification. Methanol, ethanol or their mixtures favor the pressure-induced wrinkle formation in SiO 2 /Si substrates. In these cases, the pressure evolution of the delamination process follows a universal behavior independently of the number of graphene layers with a complete delamination at /sim4GPa. The quantitative analysis of the wrinkle geometry evolution can be consistently interpreted as due to a pressure-induced increase of the bending stiffness of the graphene stacks, or a reduction of the adhesion forces between the sample and the substrate, or both. These results should also be of practical use in high-pressure experiments of van der Waals systems.

show abstract

“…Diamond particles with different sizes are considered as perspective reinforcements for AMC. Diamond is one of the carbon allotropes [19] with a high hardness of about 100 GPa and very high thermal conductivity up to 2200 W/(m K). Diamonds are characterized by high Young's modulus, electrical resistivity, and good chemical stability; such properties make this material interesting for usage as an additive for metal matrix composites.…”

Section: Introductionmentioning

confidence: 99%

Laser Fusion of Aluminum Powder Coated with Diamond Particles via Selective Laser Melting: Powder Preparation and Synthesis Description

et al. 2021

View full text Add to dashboard Cite

This work aims to study the possibility of obtaining Al–C composite from AlSi10MgCu aluminum matrix with the addition of 500 nm-sized diamond particles by selective laser melting (SLM) process. Al–C composite powder was prepared by mechanical mixing to form a uniform cover along the surface of aluminum particles. The diamond content in the resulting AlSi10MgCu-diamond composite powder was equal to 0.67 wt %. The selection of the optimal SLM parameters for the obtained composite material is presented. For materials characterization, the following methods were used: scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), and Raman spectroscopy. X-ray photoelectron spectroscopy (XPS) was applied after SLM printing for a detailed investigation of the obtained composites. The presence of carbon additives and the formation of aluminum carbides in the material after the SLM process were demonstrated.

show abstract

Carbon under pressure

Cited by 85 publications

References 662 publications

Machine learning force fields based on local parametrization of dispersion interactions: Application to the phase diagram of C60

Machine learning force fields based on local parametrization of dispersion interactions: Application to the phase diagram of C60

Delamination of multilayer graphene stacks from its substrate through wrinkle formation under high pressures

Laser Fusion of Aluminum Powder Coated with Diamond Particles via Selective Laser Melting: Powder Preparation and Synthesis Description

Contact Info

Product

Resources

About