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Persistent Identifier
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perma:LIST.UHCCHQ |
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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-v3
OpenAlex ID: https://openalex.org/W4283702080 |
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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 when reaction mechanisms and products are not thoroughly understood. Here we report new methods of CRN generation and analysis that seek to overcome these limitations. We construct CRNs by enumerating and then filtering all stoichiometrically valid reactions, avoiding the need to know reaction templates a priori. By applying efficient stochastic algorithms, we can 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 occur during SEI formation. This methodology enables the de novo exploration of vast chemical spaces, with the potential for diverse applications throughout electrochemistry. (2022-06-29)
***This entry has been automatically imported via OpenAlex by LIST harvest scripts. Please refer to https://doi.org/10.26434/chemrxiv-2021-c2gp3-v3 for the original and latest version of the publication*** (2026-07-01) |
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Subject
| Chemistry; Engineering; Physics |
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Keyword
| Computer science
A priori and a posteriori
Construct (python library)
Biochemical engineering
Filter (signal processing)
Engineering |
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Topic Classification
| Machine Learning in Materials Science
Advanced Battery Technologies Research
Electrochemical Analysis and Applications |
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Deposit Date
| 2022-06-29 |
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Data Type
| Preprint |
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Data Source
| ChemRxiv |
