Towards an atomistic understanding of disordered carbon electrode materials

Deringer, Volker L.; Merlet, Céline; Hu, Yuchen; Lee, Tae Hoon; Kattirtzi, John A.; Pecher, Oliver; Csänyi, Gábor; Elliott, S. R.; Grey, Clare P.

doi:10.1039/c8cc01388h

Cited by 95 publications

(131 citation statements)

References 43 publications

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“…In an initial communication, 33 we have recently argued that carbon-based energy materials can be accurately described in atomistic simulations with a machine learning (ML) based interatomic potential for carbon. 34 Such ML potentials perform a high-dimensional fit to complex potential energy surfaces and allow for materials modelling with close-to-DFT accuracy but require only a small fraction of the cost; [35][36][37][38][39] we recently demonstrated the large synergy between ML and DFT methods for functionalised amorphous carbon materials.…”

Section: Introductionmentioning

confidence: 99%

First-principles study of alkali-metal intercalation in disordered carbon anode materials

et al. 2019

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Section: Introductionmentioning

confidence: 99%

First-principles study of alkali-metal intercalation in disordered carbon anode materials

et al. 2019

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“…With regard to applications in battery materials, disordered and "porous" carbons have been studied using the aforementioned carbon potential, [163] following ideas initially pursued with empirical interatomic potentials. [153,164] Building on a computational description of pure disordered carbon, it was also shown how the insertion of Li ions in carbon can be described by a difference potential.…”

Section: Carbon Nanomaterialsmentioning

confidence: 99%

“…b) TEM micrographs of three different carbon materials as used in electrochemistry, differing in their structural order ("graphite-like-ness") which increases from left to right. [163] Copyright 2018, The Authors, published by The Royal Society of Chemistry. [150] Copyright 2017, American Chemical Society.…”

Section: Challenges and Future Directionsmentioning

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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“…7 Two characteristic features are observed on a reduction voltage profile; a sloping region followed by a plateau region near to 0 V versus metallic sodium. 4 Recent ab initio studies have suggested that Na ions with an ionic nature are trapped at defects sites on carbon for the sloping region, 8,9 and clustering of Na ions and the formation of Na-Na bonds in nanopores at the plateau region. 9 The formation of metallic-like Na in hard carbon has been also evidenced by operando 23 Na solid-state NMR study.…”

Section: Introductionmentioning

confidence: 99%

“…4 Recent ab initio studies have suggested that Na ions with an ionic nature are trapped at defects sites on carbon for the sloping region, 8,9 and clustering of Na ions and the formation of Na-Na bonds in nanopores at the plateau region. 9 The formation of metallic-like Na in hard carbon has been also evidenced by operando 23 Na solid-state NMR study. 10 Nevertheless, solvated Na ions by diglyme are reversibly inserted into graphite, forming staging compounds.…”

Section: Introductionmentioning

confidence: 99%

Li/Na Storage Properties of Disordered Carbons Synthesized by Mechanical Milling

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Impact of structural disorder induced by mechanical milling on electrochemical properties of carbonaceous materials is systematically examined. Carbonaceous materials with different structural disorder are prepared by mechanical milling with different rotation speeds. Thus prepared carbons show sloping voltage profiles in Li/Na cells, which resemble those of soft carbons. The disordered carbon material prepared at 600 rpm shows a reversible capacity of >500 mA h g −1 in a Li cell. In addition, the disordered carbon also delivers 200 mA h g −1 in a Na cell with high Coulombic efficiency for continuous cycles. Heat treatment of the disordered carbon results in the formation of hard carbon with nanopores, and thus Li/Na storage properties are significantly changed.

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Towards an atomistic understanding of disordered carbon electrode materials

Abstract: Machine-learning and DFT modelling, linked to experimental knowledge, yield new insight into the structures and reactivity of carbonaceous energy materials.

Cited by 95 publications

References 43 publications

First-principles study of alkali-metal intercalation in disordered carbon anode materials

First-principles study of alkali-metal intercalation in disordered carbon anode materials

Machine Learning Interatomic Potentials as Emerging Tools for Materials Science

Li/Na Storage Properties of Disordered Carbons Synthesized by Mechanical Milling

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