2022
DOI: 10.26434/chemrxiv-2022-5tlrz
|View full text |Cite
Preprint
|
Sign up to set email alerts
|

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

Abstract: Electrolyte solutions play a vital role in a vast range of important materials chemistry applications. For example, they are a crucial component in batteries, fuel cells, supercapacitors, electrolysis and carbon dioxide conversion/capture. Unfortunately, the determination of even their most basic properties from first principles remains an unsolved problem. As a result, the discovery and optimisation of electrolyte solutions for these applications largely relies on chemical intuition, experimental trial and er… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1

Citation Types

0
0
0

Year Published

2023
2023
2024
2024

Publication Types

Select...
1
1

Relationship

0
2

Authors

Journals

citations
Cited by 2 publications
(2 citation statements)
references
References 113 publications
0
0
0
Order By: Relevance
“…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%
“…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%
“…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%