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Persistent Identifier
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perma:LIST.L95D21 |
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Publication Date
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2026-07-06 |
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Title
| Predictive stochastic analysis of massive filter-based electrochemical reaction networks [* Cross-Reference *] |
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Other Identifier
| https://doi.org/10.26434/chemrxiv-2021-c2gp3-v4
OpenAlex ID: https://openalex.org/W4308472667 |
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Author
| Daniel Barter (Lawrence Berkeley National Laboratory) - ORCID: https://orcid.org/0000-0002-6423-117X
Evan Walter Clark Spotte‐Smith (Lawrence Berkeley National Laboratory, University of California, Berkeley) - ORCID: https://orcid.org/0000-0003-1554-197X
Nikita S. Redkar (Lawrence Berkeley National Laboratory, University of California, Berkeley) - ORCID: https://orcid.org/0000-0001-7824-8987
Aniruddh Khanwale (Lawrence Berkeley National Laboratory, University of California, Berkeley)
Shyam Dwaraknath (Luxembourg Institute of Science and Technology) - ORCID: https://orcid.org/0000-0003-0289-2607
Kristin A. Persson (Lawrence Berkeley National Laboratory, University of California, Berkeley) - ORCID: https://orcid.org/0000-0003-2495-5509
Samuel M. Blau (Lawrence Berkeley National Laboratory) - ORCID: https://orcid.org/0000-0003-3132-3032 |
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Point of Contact
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Use email button above to contact.
LIST QDKM (LIST) |
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Description
| Chemical reaction networks (CRNs) are powerful tools for obtaining insight into complex reactive processes. However, they are difficult to employ in domains such as electrochemistry where reaction mechanisms and outcomes are not well understood. To overcome these limitations, we report new methods to assist in CRN construction and analysis. Beginning with a known set of potentially relevant species, we enumerate and then filter all stoichiometrically valid reactions, constructing CRNs without reaction templates. By applying efficient stochastic algorithms, we can then interrogate CRNs to predict network products and reveal reaction pathways to species of interest. We apply this methodology to study solid-electrolyte interphase (SEI) formation in Li-ion batteries, automatically recovering products from the literature and predicting previously unknown species. We validate these results by combining CRN-predicted pathways with first-principles mechanistic analysis, discovering novel mechanisms which could realistically contribute to SEI formation. This methodology enables the exploration of vast chemical spaces, with the potential for applications throughout electrochemistry. (2022-11-07)
***This entry has been automatically imported via OpenAlex by LIST harvest scripts. Please refer to https://doi.org/10.26434/chemrxiv-2021-c2gp3-v4 for the original and latest version of the publication*** (2026-07-01) |
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Subject
| Earth and Environmental Sciences; Engineering; Physics |
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Keyword
| Computer science
Set (abstract data type)
Electrochemistry
Filter (signal processing)
Biochemical engineering
Biological system
Chemistry
Electrode
Engineering |
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Topic Classification
| Machine Learning in Materials Science
Electrocatalysts for Energy Conversion
Advanced Battery Technologies Research |
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Deposit Date
| 2022-11-07 |
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Data Type
| Preprint |
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Data Source
| ChemRxiv |
