Property trends in simple metals: An empirical potential approach

Nichol, Alan; Ackland, Graeme J.

doi:10.1103/physrevb.93.184101

Cited by 43 publications

(20 citation statements)

References 27 publications

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“…The interatomic potential of Nichol et al. [ 54 ] was used for Li metal, which was fitted to cohesive and vacancy energies, elastic moduli, the lattice parameter, and crystal stability. The SE was rigid with fixed atom positions.…”

Section: Methodsmentioning

confidence: 99%

Interfacial Atomistic Mechanisms of Lithium Metal Stripping and Plating in Solid‐State Batteries

et al. 2021

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All‐solid‐state batteries based on a Li metal anode represent a promising next‐generation energy storage system, but are currently limited by low current density and short cycle life. Further research to improve the Li metal anode is impeded by the lack of understanding in its failure mechanisms at lithium–solid interfaces, in particular, the fundamental atomistic processes responsible for interface failure. Here, using large‐scale molecular dynamics simulations, the first atomistic modeling study of lithium stripping and plating on a solid electrolyte is performed by explicitly considering key fundamental atomistic processes and interface atomistic structures. In the simulations, the interface failure initiated with the formation of nano‐sized pores, and how interface structures, lithium diffusion, adhesion energy, and applied pressure affect interface failure during Li cycling are observed. By systematically varying the parameters of solid‐state lithium cells in the simulations, the parameter space of applied pressures and interfacial adhesion energies that inhibit interface failure during cycling are mapped to guide selection of solid‐state cells. This study establishes the atomistic modeling for Li stripping and plating, and predicts optimal solid interfaces and new strategies for the future research and development of solid‐state Li‐metal batteries.

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Section: Methodsmentioning

confidence: 99%

Interfacial Atomistic Mechanisms of Lithium Metal Stripping and Plating in Solid‐State Batteries

et al. 2021

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“…3. Mean absolute errors in (a) predicted energies (b) predicted forces for all four ML-IAPs as well as traditional IAPs (EAM[75,76], MEAM[77][78][79], Tersoff[80,81]). The upper left and lower right triangles within each cell represent training and test errors, respectively.improvements in accuracy, especially in predicted energies, are observed for the NNP and qSNAP models.…”

mentioning

confidence: 99%

Performance and Cost Assessment of Machine Learning Interatomic Potentials

et al. 2020

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Machine learning of the quantitative relationship between local environment descriptors and the potential energy surface of a system of atoms has emerged as a new frontier in the development of interatomic potentials (IAPs). Here, we present a comprehensive evaluation of ML-IAPs based on four local environment descriptors -Behler-Parrinello symmetry functions, smooth overlap of atomic positions (SOAP), the Spectral Neighbor Analysis Potential (SNAP) bispectrum components, and moment tensors -using a diverse data set generated using high-throughput density functional theory (DFT) calculations. The data set comprising bcc (Li, Mo) and fcc (Cu, Ni) metals and diamond group IV semiconductors (Si, Ge) is chosen to span a range of crystal structures and bonding. All descriptors studied show excellent performance in predicting energies and forces far surpassing that of classical IAPs, as well as predicting properties such as elastic constants and phonon dispersion curves. We observe a general trade-off between accuracy and the degrees of freedom of each model, and consequently computational cost. We will discuss these trade-offs in the context of model selection for molecular dynamics and other applications.

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“…For dispersion correction, the MBD approach was chosen [ 31 , 32 ]. MD simulations for generating the training set and validation structures were performed in the NVE ensemble at 300 K and 1000 K using the LAMMPS code [ 33 ] with the embedded atom method (EAM) potential for alkali metals developed by Nichol and Ackland [ 34 ].…”

Section: Materials and Methodsmentioning

confidence: 99%

Accessing Structural, Electronic, Transport and Mesoscale Properties of Li-GICs via a Complete DFTB Model with Machine-Learned Repulsion Potential

et al. 2021

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Lithium-graphite intercalation compounds (Li-GICs) are the most popular anode material for modern lithium-ion batteries and have been subject to numerous studies—both experimental and theoretical. However, the system is still far from being consistently understood in detail across the full range of state of charge (SOC). The performance of approaches based on density functional theory (DFT) varies greatly depending on the choice of functional, and their computational cost is far too high for the large supercells necessary to study dilute and non-equilibrium configurations which are of paramount importance for understanding a complete charging cycle. On the other hand, cheap machine learning methods have made some progress in predicting, e.g., formation energetics, but fail to provide the full picture, including electrostatics and migration barriers. Following up on our previous work, we deliver on the promise of providing a complete and affordable simulation framework for Li-GICs. It is based on density functional tight binding (DFTB), which is fitted to dispersion-corrected DFT data using Gaussian process regression (GPR). In this work, we added the previously neglected lithium–lithium repulsion potential and extend the training set to include superdense Li-GICs (LiC6−x; x>0) and lithium metal, allowing for the investigation of dendrite formation, next-generation modified GIC anodes, and non-equilibrium states during fast charging processes in the future. For an extended range of structural and energetic properties—layer spacing, bond lengths, formation energies and migration barriers—our method compares favorably with experimental results and with state-of-the-art dispersion-corrected DFT at a fraction of the computational cost. We make use of this by investigating some larger-scale system properties—long range Li–Li interactions, dielectric constants and domain-formation—proving our method’s capability to bring to light new insights into the Li-GIC system and bridge the gap between DFT and meso-scale methods such as cluster expansions and kinetic Monte Carlo simulations.

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Property trends in simple metals: An empirical potential approach

Cited by 43 publications

References 27 publications

Interfacial Atomistic Mechanisms of Lithium Metal Stripping and Plating in Solid‐State Batteries

Interfacial Atomistic Mechanisms of Lithium Metal Stripping and Plating in Solid‐State Batteries

Performance and Cost Assessment of Machine Learning Interatomic Potentials

Accessing Structural, Electronic, Transport and Mesoscale Properties of Li-GICs via a Complete DFTB Model with Machine-Learned Repulsion Potential

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