Interatomic potentials: achievements and challenges

Müser, Martin H.; Sukhomlinov, Sergey V.; Pastewka, Lars

doi:10.1080/23746149.2022.2093129

Cited by 27 publications

(17 citation statements)

References 405 publications

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“…43,73,74,172 The Buckingham potential or modified forms of it such as the Beest−Kramer−van Santen (BKS) potential, 448 have been used extensively in molecular dynamics simulations. 449,450 For recent discussions of different interaction potentials, see for example Lim, 451 Araujo and Ballester, 45 and Muser et al 452 Where many-body forces become important, such as the Axilrod−Teller−Muto term and beyond, 68,69,453 as for example in metallic systems, where the many-body expansion is slowly converging, 50 one has to apply different potentials incorporating many-body forces in an effective way. Glue potentials 454 and the Sutton−Chen potential 455 are more appropriate than LJ, and ideally we would use more accurate quantum-theoretical methods, which are computationally demanding.…”

Section: Discussionmentioning

confidence: 99%

100 Years of the Lennard-Jones Potential

Schwerdtfeger,

Wales

2024

J. Chem. Theory Comput.

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Section: Discussionmentioning

confidence: 99%

100 Years of the Lennard-Jones Potential

Schwerdtfeger,

Wales

2024

J. Chem. Theory Comput.

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“…For ionic and semi-ionic solids, the total energy can be partitioned into two terms: a long-range Coulombic term and a short-range term that accounts for Pauli repulsion and covalent and dispersive attractive interactions. A range of force field forms are used to represent these short-range interactions, and they can be fitted to experimental data and/or results from first-principles simulations. − Proven force fields are available for a wide range of solid electrolyte materials, including oxides , and sulfides. , …”

Section: Methodsmentioning

confidence: 99%

Going against the Grain: Atomistic Modeling of Grain Boundaries in Solid Electrolytes for Solid-State Batteries

Dawson

2023

ACS Mater. Au

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Atomistic modeling techniques, including density functional theory and molecular dynamics, play a critical role in the understanding, design, discovery, and optimization of bulk solid electrolyte materials for solid-state batteries. In contrast, despite the fact that the atomistic simulation of microstructural inhomogeneities, such as grain boundaries, can reveal essential information regarding the performance of solid electrolytes, such simulations have so far only been limited to a relatively small selection of materials. In this Perspective, the fundamental properties of grain boundaries in solid electrolytes that can be determined and manipulated through state-of-the-art atomistic modeling are illustrated through recent studies in the literature. The insights and examples presented here will inspire future computational studies of grain boundaries with the aim of overcoming their often detrimental impact on ion transport and dendrite growth inhibition in solid electrolytes.

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“…149 This approach not only has high computational accuracy and efficiency, but also does not require an unaffordable computational cost. 150,151 This approach views molecules as a series of interactions between atoms, and predicts the properties of molecules by learning these interaction patterns and rules. 152 For example, MLIPs can be utilized to forecast the geometric configuration, vibrational frequencies, and thermodynamic properties of molecules.…”

Section: Machine Learning-based Performance Predictionmentioning

confidence: 99%