Zero-Shot Discovery of High-Performance, Low-Cost Organic Battery Materials Using Machine Learning
Organic electrode materials (OEMs), composed of abundant elements such as carbon, nitrogen, and oxygen, offer sustainable alternatives to conventional electrode materials that depend on finite metal resources. The vast structural diversity of organic compounds provides a virtually unlimited design s...
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2024
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| _version_ | 1852025488005922816 |
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| author | Jaehyun Park (2402962) |
| author2 | Farshud Sorourifar (14553482) Madhav R. Muthyala (20026184) Abigail M. Houser (20026187) Madison Tuttle (20026190) Joel A. Paulson (1631845) Shiyu Zhang (372275) |
| author2_role | author author author author author author |
| author_facet | Jaehyun Park (2402962) Farshud Sorourifar (14553482) Madhav R. Muthyala (20026184) Abigail M. Houser (20026187) Madison Tuttle (20026190) Joel A. Paulson (1631845) Shiyu Zhang (372275) |
| author_role | author |
| dc.creator.none.fl_str_mv | Jaehyun Park (2402962) Farshud Sorourifar (14553482) Madhav R. Muthyala (20026184) Abigail M. Houser (20026187) Madison Tuttle (20026190) Joel A. Paulson (1631845) Shiyu Zhang (372275) |
| dc.date.none.fl_str_mv | 2024-11-01T11:03:52Z |
| dc.identifier.none.fl_str_mv | 10.1021/jacs.4c11663.s002 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Zero-Shot_Discovery_of_High-Performance_Low-Cost_Organic_Battery_Materials_Using_Machine_Learning/27447206 |
| dc.rights.none.fl_str_mv | CC BY-NC 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biochemistry Medicine Pharmacology Science Policy Infectious Diseases Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified vast structural diversity simultaneously balance reward organic compounds provides offer sustainable alternatives human intuition alone finite metal resources conventional electrode materials combines computational chemistry 000 organic compounds performing oems among oems ), composed solubility ), oems selected discovered oems among sparkle synthesizability ). six years shot predictions shot discovery new framework molecular generation machine learning fold improvement extrapolation tasks experimental testing error approaches edisonian trial cycling stability cell batteries abundant elements |
| dc.title.none.fl_str_mv | Zero-Shot Discovery of High-Performance, Low-Cost Organic Battery Materials Using Machine Learning |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | Organic electrode materials (OEMs), composed of abundant elements such as carbon, nitrogen, and oxygen, offer sustainable alternatives to conventional electrode materials that depend on finite metal resources. The vast structural diversity of organic compounds provides a virtually unlimited design space; however, exploring this space through Edisonian trial-and-error approaches is costly and time-consuming. In this work, we develop a new framework, SPARKLE, that combines computational chemistry, molecular generation, and machine learning to achieve zero-shot predictions of OEMs that simultaneously balance reward (specific energy), risk (solubility), and cost (synthesizability). We demonstrate that SPARKLE significantly outperforms alternative black-box machine learning algorithms on interpolation and extrapolation tasks. By deploying SPARKLE over a design space of more than 670,000 organic compounds, we identified ≈5000 novel OEM candidates. Twenty-seven of them were synthesized and fabricated into coin-cell batteries for experimental testing. Among SPARKLE-discovered OEMs, 62.9% exceeded benchmark performance metrics, representing a 3-fold improvement over OEMs selected by human intuition alone (20.8% based on six years of prior lab experience). The top-performing OEMs among the 27 candidates exhibit specific energy and cycling stability that surpass the state-of-the-art while being synthesizable at a fraction of the cost. |
| eu_rights_str_mv | openAccess |
| id | Manara_7b2dd5c5a30dbebcdd0fb1dd42e49ade |
| identifier_str_mv | 10.1021/jacs.4c11663.s002 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/27447206 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY-NC 4.0 |
| spelling | Zero-Shot Discovery of High-Performance, Low-Cost Organic Battery Materials Using Machine LearningJaehyun Park (2402962)Farshud Sorourifar (14553482)Madhav R. Muthyala (20026184)Abigail M. Houser (20026187)Madison Tuttle (20026190)Joel A. Paulson (1631845)Shiyu Zhang (372275)BiochemistryMedicinePharmacologyScience PolicyInfectious DiseasesBiological Sciences not elsewhere classifiedChemical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedvast structural diversitysimultaneously balance rewardorganic compounds providesoffer sustainable alternativeshuman intuition alonefinite metal resourcesconventional electrode materialscombines computational chemistry000 organic compoundsperforming oems amongoems ), composedsolubility ),oems selecteddiscovered oemsamong sparklesynthesizability ).six yearsshot predictionsshot discoverynew frameworkmolecular generationmachine learningfold improvementextrapolation tasksexperimental testingerror approachesedisonian trialcycling stabilitycell batteriesabundant elementsOrganic electrode materials (OEMs), composed of abundant elements such as carbon, nitrogen, and oxygen, offer sustainable alternatives to conventional electrode materials that depend on finite metal resources. The vast structural diversity of organic compounds provides a virtually unlimited design space; however, exploring this space through Edisonian trial-and-error approaches is costly and time-consuming. In this work, we develop a new framework, SPARKLE, that combines computational chemistry, molecular generation, and machine learning to achieve zero-shot predictions of OEMs that simultaneously balance reward (specific energy), risk (solubility), and cost (synthesizability). We demonstrate that SPARKLE significantly outperforms alternative black-box machine learning algorithms on interpolation and extrapolation tasks. By deploying SPARKLE over a design space of more than 670,000 organic compounds, we identified ≈5000 novel OEM candidates. Twenty-seven of them were synthesized and fabricated into coin-cell batteries for experimental testing. Among SPARKLE-discovered OEMs, 62.9% exceeded benchmark performance metrics, representing a 3-fold improvement over OEMs selected by human intuition alone (20.8% based on six years of prior lab experience). The top-performing OEMs among the 27 candidates exhibit specific energy and cycling stability that surpass the state-of-the-art while being synthesizable at a fraction of the cost.2024-11-01T11:03:52ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1021/jacs.4c11663.s002https://figshare.com/articles/dataset/Zero-Shot_Discovery_of_High-Performance_Low-Cost_Organic_Battery_Materials_Using_Machine_Learning/27447206CC BY-NC 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/274472062024-11-01T11:03:52Z |
| spellingShingle | Zero-Shot Discovery of High-Performance, Low-Cost Organic Battery Materials Using Machine Learning Jaehyun Park (2402962) Biochemistry Medicine Pharmacology Science Policy Infectious Diseases Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified vast structural diversity simultaneously balance reward organic compounds provides offer sustainable alternatives human intuition alone finite metal resources conventional electrode materials combines computational chemistry 000 organic compounds performing oems among oems ), composed solubility ), oems selected discovered oems among sparkle synthesizability ). six years shot predictions shot discovery new framework molecular generation machine learning fold improvement extrapolation tasks experimental testing error approaches edisonian trial cycling stability cell batteries abundant elements |
| status_str | publishedVersion |
| title | Zero-Shot Discovery of High-Performance, Low-Cost Organic Battery Materials Using Machine Learning |
| title_full | Zero-Shot Discovery of High-Performance, Low-Cost Organic Battery Materials Using Machine Learning |
| title_fullStr | Zero-Shot Discovery of High-Performance, Low-Cost Organic Battery Materials Using Machine Learning |
| title_full_unstemmed | Zero-Shot Discovery of High-Performance, Low-Cost Organic Battery Materials Using Machine Learning |
| title_short | Zero-Shot Discovery of High-Performance, Low-Cost Organic Battery Materials Using Machine Learning |
| title_sort | Zero-Shot Discovery of High-Performance, Low-Cost Organic Battery Materials Using Machine Learning |
| topic | Biochemistry Medicine Pharmacology Science Policy Infectious Diseases Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified vast structural diversity simultaneously balance reward organic compounds provides offer sustainable alternatives human intuition alone finite metal resources conventional electrode materials combines computational chemistry 000 organic compounds performing oems among oems ), composed solubility ), oems selected discovered oems among sparkle synthesizability ). six years shot predictions shot discovery new framework molecular generation machine learning fold improvement extrapolation tasks experimental testing error approaches edisonian trial cycling stability cell batteries abundant elements |