Flow Battery Manifold Design With Heterogeneous Inputs Through Generative Adversarial Neural Networks Article Swipe
Eric Seng
,
Hugh O’Connor
,
Adam M. Boyce
,
Josh J. Bailey
,
Anton van Beek
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1115/detc2025-168839
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1115/detc2025-168839
Generative machine learning has emerged as a powerful tool for design representation and exploration. However, its application is often constrained by the need for large datasets of existing designs and the lack of interpretability about what features drive optimality. To address these challenges, we introduce a systematic framework for constructing training datasets tailored to generative models and demonstrate how these models can be leveraged for interpretable design. The novelty of this work is twofold: (i) we present a systematic framework for generating archetypes with internally homogeneous but mutually heterogeneous inputs that can be used to generate a training dataset, and (ii) we show how integrating generative models with Bayesian optimization can enhance the interpretability of the latent space of admissible designs. These findings are validated by using the framework to design a flow battery manifold, demonstrating that it effectively captures the space of feasible designs, including novel configurations while enabling efficient exploration. This work broadens the applicability of generative machine-learning models in system designs by enhancing quality and reliability.
Related Topics
Concepts
No concepts available.
Metadata
- Type
- article
- Landing Page
- https://doi.org/10.1115/detc2025-168839
- OA Status
- gold
- OpenAlex ID
- https://openalex.org/W4415598838
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4415598838Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1115/detc2025-168839Digital Object Identifier
- Title
-
Flow Battery Manifold Design With Heterogeneous Inputs Through Generative Adversarial Neural NetworksWork title
- Type
-
articleOpenAlex work type
- Publication year
-
2025Year of publication
- Publication date
-
2025-08-17Full publication date if available
- Authors
-
Eric Seng, Hugh O’Connor, Adam M. Boyce, Josh J. Bailey, Anton van BeekList of authors in order
- Landing page
-
https://doi.org/10.1115/detc2025-168839Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1115/detc2025-168839Direct OA link when available
- Cited by
-
0Total citation count in OpenAlex
Full payload
| id | https://openalex.org/W4415598838 |
|---|---|
| doi | https://doi.org/10.1115/detc2025-168839 |
| ids.doi | https://doi.org/10.1115/detc2025-168839 |
| ids.openalex | https://openalex.org/W4415598838 |
| fwci | 0.0 |
| type | article |
| title | Flow Battery Manifold Design With Heterogeneous Inputs Through Generative Adversarial Neural Networks |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T12111 |
| topics[0].field.id | https://openalex.org/fields/22 |
| topics[0].field.display_name | Engineering |
| topics[0].score | 0.9348999857902527 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2209 |
| topics[0].subfield.display_name | Industrial and Manufacturing Engineering |
| topics[0].display_name | Industrial Vision Systems and Defect Detection |
| topics[1].id | https://openalex.org/T10663 |
| topics[1].field.id | https://openalex.org/fields/22 |
| topics[1].field.display_name | Engineering |
| topics[1].score | 0.9318000078201294 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2203 |
| topics[1].subfield.display_name | Automotive Engineering |
| topics[1].display_name | Advanced Battery Technologies Research |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| language | |
| locations[0].id | doi:10.1115/detc2025-168839 |
| locations[0].is_oa | True |
| locations[0].source | |
| locations[0].license | cc-by |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | proceedings-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Volume 3A: 51st Design Automation Conference (DAC) |
| locations[0].landing_page_url | https://doi.org/10.1115/detc2025-168839 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5120153662 |
| authorships[0].author.orcid | |
| authorships[0].author.display_name | Eric Seng |
| authorships[0].countries | US |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I170897317 |
| authorships[0].affiliations[0].raw_affiliation_string | Duke University , , , |
| authorships[0].institutions[0].id | https://openalex.org/I170897317 |
| authorships[0].institutions[0].ror | https://ror.org/00py81415 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I170897317 |
| authorships[0].institutions[0].country_code | US |
| authorships[0].institutions[0].display_name | Duke University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Eric Seng |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Duke University , , , |
| authorships[1].author.id | https://openalex.org/A5025032291 |
| authorships[1].author.orcid | https://orcid.org/0000-0003-2437-7534 |
| authorships[1].author.display_name | Hugh O’Connor |
| authorships[1].countries | GB |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I126231945 |
| authorships[1].affiliations[0].raw_affiliation_string | Queen’s University Belfast , , , |
| authorships[1].institutions[0].id | https://openalex.org/I126231945 |
| authorships[1].institutions[0].ror | https://ror.org/00hswnk62 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I126231945 |
| authorships[1].institutions[0].country_code | GB |
| authorships[1].institutions[0].display_name | Queen's University Belfast |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Hugh O’Connor |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Queen’s University Belfast , , , |
| authorships[2].author.id | https://openalex.org/A5074762244 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-8164-1808 |
| authorships[2].author.display_name | Adam M. Boyce |
| authorships[2].countries | IE |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I100930933 |
| authorships[2].affiliations[0].raw_affiliation_string | University College Dublin , , |
| authorships[2].institutions[0].id | https://openalex.org/I100930933 |
| authorships[2].institutions[0].ror | https://ror.org/05m7pjf47 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I100930933 |
| authorships[2].institutions[0].country_code | IE |
| authorships[2].institutions[0].display_name | University College Dublin |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Adam Boyce |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | University College Dublin , , |
| authorships[3].author.id | https://openalex.org/A5008063096 |
| authorships[3].author.orcid | https://orcid.org/0000-0003-2397-6690 |
| authorships[3].author.display_name | Josh J. Bailey |
| authorships[3].countries | GB |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I126231945 |
| authorships[3].affiliations[0].raw_affiliation_string | Queen’s University Belfast , , , |
| authorships[3].institutions[0].id | https://openalex.org/I126231945 |
| authorships[3].institutions[0].ror | https://ror.org/00hswnk62 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I126231945 |
| authorships[3].institutions[0].country_code | GB |
| authorships[3].institutions[0].display_name | Queen's University Belfast |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Josh J. Bailey |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Queen’s University Belfast , , , |
| authorships[4].author.id | https://openalex.org/A5014228963 |
| authorships[4].author.orcid | https://orcid.org/0000-0001-6438-8751 |
| authorships[4].author.display_name | Anton van Beek |
| authorships[4].countries | IE |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I100930933 |
| authorships[4].affiliations[0].raw_affiliation_string | University College Dublin , , |
| authorships[4].institutions[0].id | https://openalex.org/I100930933 |
| authorships[4].institutions[0].ror | https://ror.org/05m7pjf47 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I100930933 |
| authorships[4].institutions[0].country_code | IE |
| authorships[4].institutions[0].display_name | University College Dublin |
| authorships[4].author_position | last |
| authorships[4].raw_author_name | Anton van Beek |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | University College Dublin , , |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.1115/detc2025-168839 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-28T00:00:00 |
| display_name | Flow Battery Manifold Design With Heterogeneous Inputs Through Generative Adversarial Neural Networks |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T12111 |
| primary_topic.field.id | https://openalex.org/fields/22 |
| primary_topic.field.display_name | Engineering |
| primary_topic.score | 0.9348999857902527 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2209 |
| primary_topic.subfield.display_name | Industrial and Manufacturing Engineering |
| primary_topic.display_name | Industrial Vision Systems and Defect Detection |
| cited_by_count | 0 |
| locations_count | 1 |
| best_oa_location.id | doi:10.1115/detc2025-168839 |
| best_oa_location.is_oa | True |
| best_oa_location.source | |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | proceedings-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Volume 3A: 51st Design Automation Conference (DAC) |
| best_oa_location.landing_page_url | https://doi.org/10.1115/detc2025-168839 |
| primary_location.id | doi:10.1115/detc2025-168839 |
| primary_location.is_oa | True |
| primary_location.source | |
| primary_location.license | cc-by |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | proceedings-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Volume 3A: 51st Design Automation Conference (DAC) |
| primary_location.landing_page_url | https://doi.org/10.1115/detc2025-168839 |
| publication_date | 2025-08-17 |
| publication_year | 2025 |
| referenced_works_count | 0 |
| abstract_inverted_index.a | 7, 46, 78, 97, 132 |
| abstract_inverted_index.To | 40 |
| abstract_inverted_index.as | 6 |
| abstract_inverted_index.be | 63, 93 |
| abstract_inverted_index.by | 21, 126, 165 |
| abstract_inverted_index.in | 162 |
| abstract_inverted_index.is | 18, 73 |
| abstract_inverted_index.it | 138 |
| abstract_inverted_index.of | 27, 33, 70, 115, 119, 143, 158 |
| abstract_inverted_index.to | 54, 95, 130 |
| abstract_inverted_index.we | 44, 76, 102 |
| abstract_inverted_index.(i) | 75 |
| abstract_inverted_index.The | 68 |
| abstract_inverted_index.and | 13, 30, 57, 100, 168 |
| abstract_inverted_index.are | 124 |
| abstract_inverted_index.but | 87 |
| abstract_inverted_index.can | 62, 92, 111 |
| abstract_inverted_index.for | 10, 24, 49, 65, 81 |
| abstract_inverted_index.has | 4 |
| abstract_inverted_index.how | 59, 104 |
| abstract_inverted_index.its | 16 |
| abstract_inverted_index.the | 22, 31, 113, 116, 128, 141, 156 |
| abstract_inverted_index.(ii) | 101 |
| abstract_inverted_index.This | 153 |
| abstract_inverted_index.flow | 133 |
| abstract_inverted_index.lack | 32 |
| abstract_inverted_index.need | 23 |
| abstract_inverted_index.show | 103 |
| abstract_inverted_index.that | 91, 137 |
| abstract_inverted_index.this | 71 |
| abstract_inverted_index.tool | 9 |
| abstract_inverted_index.used | 94 |
| abstract_inverted_index.what | 36 |
| abstract_inverted_index.with | 84, 108 |
| abstract_inverted_index.work | 72, 154 |
| abstract_inverted_index.These | 122 |
| abstract_inverted_index.about | 35 |
| abstract_inverted_index.drive | 38 |
| abstract_inverted_index.large | 25 |
| abstract_inverted_index.novel | 147 |
| abstract_inverted_index.often | 19 |
| abstract_inverted_index.space | 118, 142 |
| abstract_inverted_index.these | 42, 60 |
| abstract_inverted_index.using | 127 |
| abstract_inverted_index.while | 149 |
| abstract_inverted_index.design | 11, 131 |
| abstract_inverted_index.inputs | 90 |
| abstract_inverted_index.latent | 117 |
| abstract_inverted_index.models | 56, 61, 107, 161 |
| abstract_inverted_index.system | 163 |
| abstract_inverted_index.address | 41 |
| abstract_inverted_index.battery | 134 |
| abstract_inverted_index.design. | 67 |
| abstract_inverted_index.designs | 29, 164 |
| abstract_inverted_index.emerged | 5 |
| abstract_inverted_index.enhance | 112 |
| abstract_inverted_index.machine | 2 |
| abstract_inverted_index.novelty | 69 |
| abstract_inverted_index.present | 77 |
| abstract_inverted_index.quality | 167 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Bayesian | 109 |
| abstract_inverted_index.However, | 15 |
| abstract_inverted_index.broadens | 155 |
| abstract_inverted_index.captures | 140 |
| abstract_inverted_index.dataset, | 99 |
| abstract_inverted_index.datasets | 26, 52 |
| abstract_inverted_index.designs, | 145 |
| abstract_inverted_index.designs. | 121 |
| abstract_inverted_index.enabling | 150 |
| abstract_inverted_index.existing | 28 |
| abstract_inverted_index.feasible | 144 |
| abstract_inverted_index.features | 37 |
| abstract_inverted_index.findings | 123 |
| abstract_inverted_index.generate | 96 |
| abstract_inverted_index.learning | 3 |
| abstract_inverted_index.mutually | 88 |
| abstract_inverted_index.powerful | 8 |
| abstract_inverted_index.tailored | 53 |
| abstract_inverted_index.training | 51, 98 |
| abstract_inverted_index.twofold: | 74 |
| abstract_inverted_index.efficient | 151 |
| abstract_inverted_index.enhancing | 166 |
| abstract_inverted_index.framework | 48, 80, 129 |
| abstract_inverted_index.including | 146 |
| abstract_inverted_index.introduce | 45 |
| abstract_inverted_index.leveraged | 64 |
| abstract_inverted_index.manifold, | 135 |
| abstract_inverted_index.validated | 125 |
| abstract_inverted_index.Generative | 1 |
| abstract_inverted_index.admissible | 120 |
| abstract_inverted_index.archetypes | 83 |
| abstract_inverted_index.generating | 82 |
| abstract_inverted_index.generative | 55, 106, 159 |
| abstract_inverted_index.internally | 85 |
| abstract_inverted_index.systematic | 47, 79 |
| abstract_inverted_index.application | 17 |
| abstract_inverted_index.challenges, | 43 |
| abstract_inverted_index.constrained | 20 |
| abstract_inverted_index.demonstrate | 58 |
| abstract_inverted_index.effectively | 139 |
| abstract_inverted_index.homogeneous | 86 |
| abstract_inverted_index.integrating | 105 |
| abstract_inverted_index.optimality. | 39 |
| abstract_inverted_index.constructing | 50 |
| abstract_inverted_index.exploration. | 14, 152 |
| abstract_inverted_index.optimization | 110 |
| abstract_inverted_index.reliability. | 169 |
| abstract_inverted_index.applicability | 157 |
| abstract_inverted_index.demonstrating | 136 |
| abstract_inverted_index.heterogeneous | 89 |
| abstract_inverted_index.interpretable | 66 |
| abstract_inverted_index.configurations | 148 |
| abstract_inverted_index.representation | 12 |
| abstract_inverted_index.interpretability | 34, 114 |
| abstract_inverted_index.machine-learning | 160 |
| cited_by_percentile_year | |
| countries_distinct_count | 3 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.69357496 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |