Towards predictive design of electrolyte solutions by accelerating ab initio simulation with neural networks.

Zhang, Junji; Pagotto, Joshua; Duignan, Timothy T.

doi:10.26434/chemrxiv-2022-5tlrz

Cited by 2 publications

(2 citation statements)

References 113 publications

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“…The state was found to be (strongly) metastable, which is probably due to the much lower concentration of lithium ions (and the absence of nanodomains) compared to the WiS investigated here, but it opens the possibility that such cation pairs are a common feature in concentrated and/or confined aqueous solutions. 25…”

Section: Resultsmentioning

confidence: 99%

Oligomerization of lithium ions in water-in-salt electrolytes

Goloviznina,

Serva,

Salanne

2023

Preprint

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Water-in-salts (WiS) have recently emerged as promising electrolytes for energy storage applications, ranging from aqueous batteries to supercapacitors. Here, ab initio molecular dynamics is used to study the structure of a 21 m LiTFSI WiS. The simulation reveals a new feature, in which the lithium ions form oligomer-like nanochains that involve up to 10 ions. Despite the strong Coulombic interaction between them, the ions in the chains are found at a distance of 2.5 Angstroms. They display a drastically different solvation shell compared to the isolated ions, in which they share on average two water molecules. The nanochains have a highly transient character due to the low free energy barrier for forming/breaking them. Providing new insights into the nanostructure of WiS electrolytes, our work calls for re-evaluating our current knowledge of highly concentrated electrolytes and the impact of the modification of solvation of active species on their electrochemical performances.

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Section: Resultsmentioning

confidence: 99%

Oligomerization of lithium ions in water-in-salt electrolytes

Goloviznina,

Serva,

Salanne

2023

Preprint

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“…Atomistic models developed using this method have been applied to molten LiCl [53], NaCl [44] and LiCl-KCl [54]. Such approaches provide new physical insights into the temperature-dependent coordination environment of liquids, together with property information including density, self-diffusion constants, thermal conductivity, and ionic conductivity [55].…”

Section: Experimentally Driven Mlip'smentioning

confidence: 99%

Deciphering diffuse scattering with machine learning and the equivariant foundation model: the case of molten FeO

Sivaraman,

Benmore

2024

J. Phys.: Condens. Matter

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Bridging the gap between diffuse x-ray or neutron scattering measurements and predicted structures derived from atom–atom pair potentials in disordered materials, has been a longstanding challenge in condensed matter physics. This perspective gives a brief overview of the traditional approaches employed over the past several decades. Namely, the use of approximate interatomic pair potentials that relate three-dimensional structural models to the measured structure factor and its’ associated pair distribution function. The use of machine learned interatomic potentials has grown in the past few years, and has been particularly successful in the cases of ionic and oxide systems. Recent advances in large scale sampling, along with a direct integration of scattering measurements into the model development, has provided improved agreement between experiments and large-scale models calculated with quantum mechanical accuracy. However, details of local polyhedral bonding and connectivity in meta-stable disordered systems still require improvement. Here we leverage MACE-MP-0; a newly introduced equivariant foundation model and validate the results against high-quality experimental scattering data for the case of molten iron(II) oxide (FeO). These preliminary results suggest that the emerging foundation model has the potential to surpass the traditional limitations of classical interatomic potentials.

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Towards predictive design of electrolyte solutions by accelerating ab initio simulation with neural networks.

Cited by 2 publications

References 113 publications

Oligomerization of lithium ions in water-in-salt electrolytes

Oligomerization of lithium ions in water-in-salt electrolytes

Deciphering diffuse scattering with machine learning and the equivariant foundation model: the case of molten FeO

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