Causal Relationship between Potential Shift and Molecular Structure in Concentrated Electrolytes
Understanding the origin of potential shifts in concentrated electrolytes is crucial for the design of stable and high-energy-density lithium–metal batteries. In our previous work, we identified a strong correlation between experimental electrode potential and Li<sup>+</sup>–anion intera...
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2025
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| _version_ | 1852014715827388416 |
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| author | Yumika Yokoyama (18422218) |
| author2 | Kou Nakamura (22482178) Naoto Tanibata (4108045) Hayami Takeda (4543633) Masayuki Karasuyama (9925685) Ryo Kobayashi (341637) Norio Takenaka (1426843) Atsuo Yamada (795150) Masanobu Nakayama (1417912) |
| author2_role | author author author author author author author author |
| author_facet | Yumika Yokoyama (18422218) Kou Nakamura (22482178) Naoto Tanibata (4108045) Hayami Takeda (4543633) Masayuki Karasuyama (9925685) Ryo Kobayashi (341637) Norio Takenaka (1426843) Atsuo Yamada (795150) Masanobu Nakayama (1417912) |
| author_role | author |
| dc.creator.none.fl_str_mv | Yumika Yokoyama (18422218) Kou Nakamura (22482178) Naoto Tanibata (4108045) Hayami Takeda (4543633) Masayuki Karasuyama (9925685) Ryo Kobayashi (341637) Norio Takenaka (1426843) Atsuo Yamada (795150) Masanobu Nakayama (1417912) |
| dc.date.none.fl_str_mv | 2025-11-19T16:36:13Z |
| dc.identifier.none.fl_str_mv | 10.1021/acs.jpcb.5c06002.s002 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Causal_Relationship_between_Potential_Shift_and_Molecular_Structure_in_Concentrated_Electrolytes/30657723 |
| dc.rights.none.fl_str_mv | CC BY-NC 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biophysics Biochemistry Molecular Biology Cancer Computational Biology Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified various solvent molecules select reduced sets radial distribution functions phase madelung interaction mitigate overfitting due g ., homo dominant factor governing molecular dynamics simulations intrinsic molecular properties gaussian acyclic model direct causal effect experimental electrode potential highly concentrated electrolytes concentrated electrolytes understanding intermolecular descriptors derived lingam analysis revealed high descriptor dimensionality causal discovery methods range ndf descriptors concentrated electrolytes causal discovery molecular structure based model causal relationships causal relationship potential shifts potential shift descriptors associated strong correlation previous work linear non genetic algorithm especially long data set comprehensive set |
| dc.title.none.fl_str_mv | Causal Relationship between Potential Shift and Molecular Structure in Concentrated Electrolytes |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | Understanding the origin of potential shifts in concentrated electrolytes is crucial for the design of stable and high-energy-density lithium–metal batteries. In our previous work, we identified a strong correlation between experimental electrode potential and Li<sup>+</sup>–anion interactions, suggesting the importance of intermolecular structure beyond conventional molecular properties. In this study, we revisit this issue from the perspective of causal discovery. We applied the Linear Non-Gaussian Acyclic Model (LiNGAM) to a data set of 75 electrolyte solutions containing LiFSI salt and various solvent molecules, using a comprehensive set of 132 descriptors including molecular properties (e.g., HOMO, LUMO, binding energy) and intermolecular descriptors derived from radial distribution functions (RDF) and their integrals (NDF) from molecular dynamics simulations. To mitigate overfitting due to high descriptor dimensionality, we employed a genetic algorithm to select reduced sets of descriptors. LiNGAM analysis revealed that descriptors associated with Li<sup>+</sup>–anion spatial distribution, especially long-range NDF descriptors, exhibit a direct causal effect with experimentally measured potential values. In contrast, no such causal relationships were identified for intrinsic molecular properties. These findings further support the recently proposed advanced theoretical framework, which incorporates a Debye–Hückel-based model and identifies the liquid-phase Madelung interaction as a dominant factor governing the potential shift in highly concentrated electrolytes. Moreover, we demonstrate that causal discovery methods can reveal fundamental physical mechanisms underlying electrochemical behavior. |
| eu_rights_str_mv | openAccess |
| id | Manara_e744dfc4eb4bf402ca7f899b1bf71a8b |
| identifier_str_mv | 10.1021/acs.jpcb.5c06002.s002 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30657723 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY-NC 4.0 |
| spelling | Causal Relationship between Potential Shift and Molecular Structure in Concentrated ElectrolytesYumika Yokoyama (18422218)Kou Nakamura (22482178)Naoto Tanibata (4108045)Hayami Takeda (4543633)Masayuki Karasuyama (9925685)Ryo Kobayashi (341637)Norio Takenaka (1426843)Atsuo Yamada (795150)Masanobu Nakayama (1417912)BiophysicsBiochemistryMolecular BiologyCancerComputational BiologyEnvironmental Sciences not elsewhere classifiedBiological Sciences not elsewhere classifiedChemical Sciences not elsewhere classifiedvarious solvent moleculesselect reduced setsradial distribution functionsphase madelung interactionmitigate overfitting dueg ., homodominant factor governingmolecular dynamics simulationsintrinsic molecular propertiesgaussian acyclic modeldirect causal effectexperimental electrode potentialhighly concentrated electrolytesconcentrated electrolytes understandingintermolecular descriptors derivedlingam analysis revealedhigh descriptor dimensionalitycausal discovery methodsrange ndf descriptorsconcentrated electrolytescausal discoverymolecular structurebased modelcausal relationshipscausal relationshippotential shiftspotential shiftdescriptors associatedstrong correlationprevious worklinear nongenetic algorithmespecially longdata setcomprehensive setUnderstanding the origin of potential shifts in concentrated electrolytes is crucial for the design of stable and high-energy-density lithium–metal batteries. In our previous work, we identified a strong correlation between experimental electrode potential and Li<sup>+</sup>–anion interactions, suggesting the importance of intermolecular structure beyond conventional molecular properties. In this study, we revisit this issue from the perspective of causal discovery. We applied the Linear Non-Gaussian Acyclic Model (LiNGAM) to a data set of 75 electrolyte solutions containing LiFSI salt and various solvent molecules, using a comprehensive set of 132 descriptors including molecular properties (e.g., HOMO, LUMO, binding energy) and intermolecular descriptors derived from radial distribution functions (RDF) and their integrals (NDF) from molecular dynamics simulations. To mitigate overfitting due to high descriptor dimensionality, we employed a genetic algorithm to select reduced sets of descriptors. LiNGAM analysis revealed that descriptors associated with Li<sup>+</sup>–anion spatial distribution, especially long-range NDF descriptors, exhibit a direct causal effect with experimentally measured potential values. In contrast, no such causal relationships were identified for intrinsic molecular properties. These findings further support the recently proposed advanced theoretical framework, which incorporates a Debye–Hückel-based model and identifies the liquid-phase Madelung interaction as a dominant factor governing the potential shift in highly concentrated electrolytes. Moreover, we demonstrate that causal discovery methods can reveal fundamental physical mechanisms underlying electrochemical behavior.2025-11-19T16:36:13ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1021/acs.jpcb.5c06002.s002https://figshare.com/articles/dataset/Causal_Relationship_between_Potential_Shift_and_Molecular_Structure_in_Concentrated_Electrolytes/30657723CC BY-NC 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/306577232025-11-19T16:36:13Z |
| spellingShingle | Causal Relationship between Potential Shift and Molecular Structure in Concentrated Electrolytes Yumika Yokoyama (18422218) Biophysics Biochemistry Molecular Biology Cancer Computational Biology Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified various solvent molecules select reduced sets radial distribution functions phase madelung interaction mitigate overfitting due g ., homo dominant factor governing molecular dynamics simulations intrinsic molecular properties gaussian acyclic model direct causal effect experimental electrode potential highly concentrated electrolytes concentrated electrolytes understanding intermolecular descriptors derived lingam analysis revealed high descriptor dimensionality causal discovery methods range ndf descriptors concentrated electrolytes causal discovery molecular structure based model causal relationships causal relationship potential shifts potential shift descriptors associated strong correlation previous work linear non genetic algorithm especially long data set comprehensive set |
| status_str | publishedVersion |
| title | Causal Relationship between Potential Shift and Molecular Structure in Concentrated Electrolytes |
| title_full | Causal Relationship between Potential Shift and Molecular Structure in Concentrated Electrolytes |
| title_fullStr | Causal Relationship between Potential Shift and Molecular Structure in Concentrated Electrolytes |
| title_full_unstemmed | Causal Relationship between Potential Shift and Molecular Structure in Concentrated Electrolytes |
| title_short | Causal Relationship between Potential Shift and Molecular Structure in Concentrated Electrolytes |
| title_sort | Causal Relationship between Potential Shift and Molecular Structure in Concentrated Electrolytes |
| topic | Biophysics Biochemistry Molecular Biology Cancer Computational Biology Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified various solvent molecules select reduced sets radial distribution functions phase madelung interaction mitigate overfitting due g ., homo dominant factor governing molecular dynamics simulations intrinsic molecular properties gaussian acyclic model direct causal effect experimental electrode potential highly concentrated electrolytes concentrated electrolytes understanding intermolecular descriptors derived lingam analysis revealed high descriptor dimensionality causal discovery methods range ndf descriptors concentrated electrolytes causal discovery molecular structure based model causal relationships causal relationship potential shifts potential shift descriptors associated strong correlation previous work linear non genetic algorithm especially long data set comprehensive set |