Extracting Crystal Chemistry from Amorphous Carbon Structures

Deringer, Volker L.; Csänyi, Gábor; Proserpio, Davide M.

doi:10.1002/cphc.201700151

Cited by 93 publications

(97 citation statements)

References 62 publications

(44 reference statements)

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“…Compared to the wide variety of newly identified structures [5], relatively few breakthrough applications have been realized to date. The most common usage of elementary carbon still remains burning.…”

Section: Introductionmentioning

confidence: 99%

Protomene: A new carbon allotrope

Delodovici

Manini

Wittman

et al. 2018

Carbon

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We introduce a new carbon allotrope named protomene. Its crystal structure is hexagonal, with a fully-relaxed primitive cell involving 48 atoms. Of these, 12 atoms have the potential to switch hybridization between sp 2 and sp 3 , forming dimers. By means of DFT simulations, we have identified the equilibrium structure of protomene, and estimate that it is 2% less bound than diamond. We have also estimated the amplitude of its direct band gap to be 3 eV, and predicted the X-ray diffraction pattern and phonon modes.

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Section: Introductionmentioning

confidence: 99%

Protomene: A new carbon allotrope

Delodovici

Manini

Wittman

et al. 2018

Carbon

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“…One example is to take structural snapshots from the liquid phase of a material, as sketched in Figure 1a. [34] Initially, reference databases for ML potentials have been constructed manually, which is still widely done today. For example, an ML potential fitted only to a database of liquid and amorphous carbon configurations (hence containing no prior knowledge of crystal structures, but of course some local resemblance of "tetrahedral" carbon) was shown to be suitable for crystal-structure searching in the spirit of the Ab Initio Random Structure Searching (AIRSS) [33] technique, identifying a large number of known and new hypothetical carbon allotropes.…”

Section: Ingredient 1: Reference Databasesmentioning

confidence: 99%

Machine Learning Interatomic Potentials as Emerging Tools for Materials Science

2019

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In pursuit of this goal, atomic-scale computer simulations have long been a central approach, and two major families of methods are routinely used today. On the one hand, there are quantum-mechanical simulations, in which we solve Schrödinger's equation for the electronic structure of molecular and periodic systems, most widely based on density-functional theory (DFT). [6][7][8] These methods provide (largely) reliable results for structural models of materials that normally contain a few tens or hundreds of atoms. State-of-the-art DFT methods can be applied to many material classes, and they are increasingly used for high-throughput screening and "in silico" (computer-based) design of materials: new compositions and previously unknown structures have been identified in DFT searches and subsequently experimentally realized. [5,[9][10][11] On the other hand, interatomic potential models ("force fields"), parameterizing interactions between atoms with (relatively) simple functional forms, are widely used in materials science to describe matter in molecular dynamics (MD) simulations. These simulations grant access to larger time and length scales, reaching system sizes of up to hundreds of thousands of atoms. [12] In parameterizing these potentials, a certain physical form of the atomic interactions is assumed, often in terms of bond distances, angles, and so on, and physical properties such as equilibrium lattice parameters or elastic constants enter the fitting of the potential. For this reason, such potentials are often called "empirical." They are several orders of magnitude faster than DFT, but necessarily less accurate and less easily transferable.In this Progress Report, we highlight recent developments in "machine-learned" interatomic potentials, which represent a rapidly growing field that promises to do away with the aforementioned trade-offs. Over the last year, there has been a surge of interest in machine learning (ML) methodology: part of it is due to the dramatic growth of ML throughout the scientific disciplines, and part of it is due to tangible success stories of ML-based interatomic potentials that are now beginning to emerge. We will argue that this is an exciting development with very practical implications, currently on the verge of moving from a somewhat specialized new technology to everyday applicability, poised to enhance and complement the communities' existing strengths in computational materials modeling. We will show selected applications of ML potentials to problems in materials science, discuss the current limitations (and possible pitfalls), and outline what we expect to be interesting directions for the development of the field in the coming years.Atomic-scale modeling and understanding of materials have made remarkable progress, but they are still fundamentally limited by the large computational cost of explicit electronic-structure methods such as density-functional theory. This Progress Report shows how machine learning (ML) is currently enabling a new degree of realism in materi...

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“…The initial choice for these have been many-body descriptors, most importantly the Smooth Overlap of Atomic Positions (SOAP), which includes all neighbours of an atom up to a cut-off radius. 55 To improve the stability of the t, similar to our previous work on amorphous materials modelling 40 and structure searching, 36,37 we combine the manybody SOAP expansion with non-parametric two-body ("2b") and three-body ("3b") terms that encode interatomic distances and bond angles, respectively. The 2b and SOAP descriptors have radial cut-offs of 5.0Å, whereas that for the 3b term is 2.6Å (to include only "true" bond angles involving nearest-neighbour contacts).…”

Section: Gap Ttingmentioning

confidence: 99%

“…[35][36][37][38] Such potentials are tted to reference databases of DFT energies and forces and, once generated, they allow one to perform atomistic simulations with close to DFT quality but at a computational cost that is orders of magnitude lower. 39 Indeed, we have recently shown Table 1 Experimentally known crystal structures of phosphorus.…”

Section: Introductionmentioning

confidence: 99%

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Data-driven learning and prediction of inorganic crystal structures

et al. 2018

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Crystal structure prediction algorithms, including ab initio random structure searching (AIRSS), are intrinsically limited by the huge computational cost of the underlying quantum-mechanical methods. We have recently shown that a novel class of machine learning (ML) based interatomic potentials can provide a way out: by performing a highdimensional fit to the ab initio energy landscape, these potentials reach comparable accuracy but are orders of magnitude faster. In this paper, we develop our approach, dubbed Gaussian approximation potential-based random structure searching (GAP-RSS), towards a more general tool for exploring configuration spaces and predicting structures. We present a GAP-RSS interatomic potential model for elemental phosphorus, which identifies and correctly "learns" the orthorhombic black phosphorus (A17) structure without prior knowledge of any crystalline allotropes. Using the tubular structure of fibrous phosphorus as an example, we then discuss the limits of free searching, and discuss a possible way forward that combines a recently proposed fragment analysis with GAP-RSS. Examples of possible tubular (1D) and extended (3D) hypothetical allotropes of phosphorus as found by GAP-RSS are discussed. We believe that in the future, ML potentials could become versatile and routine computational tools for materials discovery and design.

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Extracting Crystal Chemistry from Amorphous Carbon Structures

Cited by 93 publications

References 62 publications

Protomene: A new carbon allotrope

Protomene: A new carbon allotrope

Machine Learning Interatomic Potentials as Emerging Tools for Materials Science

Data-driven learning and prediction of inorganic crystal structures

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