Journal article
Machine learning assisted synthesis of lithium-ion batteries cathode materials
Nano energy, v 98, 107214
Jul 2022
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Optimizing synthesis parameters is crucial in fabricating an ideal cathode material; however, the design space is too vast to be fully explored using an Edisonian approach. Here, by clustering eleven domain-expert-derived-descriptors from literature, we use an inverse design surrogate model to build up the experimental parameters-property relationship. Without struggling with the trial-and-error method, the model enables design variables prediction that serves as an effective strategy for cathode retrosynthesis. More importantly, not only did we overcome the data scarcity problem, but the machine learning model has guided us to achieve cathode with high discharge capacity and Coulombic efficiency of 209.5 mAh/g and 86%, respectively. This work demonstrates an inverse design-to-device pipeline with unprecedented potential to accelerate the discovery of high-energy-density cathodes.
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•Inverse-design surrogate model is employed for discharge capacity prediction of lithium-ion batteries cathode materials.•Statistical imputation technique is exploited to solve the missing values and inconsistency in training data.•The proposed method enables the realization of high discharge capacity of 209.5 mAh/g with 86% coulombic efficiency.•We identified eleven descriptors from literature and reverse-engineered the synthesis parameters with high accuracy.
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Details
- Title
- Machine learning assisted synthesis of lithium-ion batteries cathode materials
- Creators
- Chi Hao Liow - Korea Advanced Institute of Science and TechnologyHyeonmuk Kang - Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 3414, Republic of KoreaSeunggu Kim - Department of Chemistry, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of KoreaMoony Na - Korea Advanced Institute of Science and TechnologyYongju Lee - Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 3414, Republic of KoreaArthur Baucour - Korea Advanced Institute of Science and TechnologyKihoon Bang - Korea Advanced Institute of Science and TechnologyYoonsu Shim - Korea Advanced Institute of Science and TechnologyJacob Choe - Korea Advanced Institute of Science and TechnologyGyuseong Hwang - Korea Advanced Institute of Science and TechnologySeongwoo Cho - Korea Advanced Institute of Science and TechnologyGun Park - Korea Advanced Institute of Science and TechnologyJiwon Yeom - Korea Advanced Institute of Science and TechnologyJoshua C. Agar - Lehigh UniversityJong Min Yuk - Korea Advanced Institute of Science and TechnologyJonghwa Shin - Korea Advanced Institute of Science and TechnologyHyuck Mo Lee - Korea Advanced Institute of Science and TechnologyHye Ryung Byon - Korea Advanced Institute of Science and TechnologyEunAe Cho - Korea Advanced Institute of Science and TechnologySeungbum Hong - Korea Advanced Institute of Science and Technology
- Publication Details
- Nano energy, v 98, 107214
- Publisher
- Elsevier
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Mechanical Engineering and Mechanics
- Web of Science ID
- WOS:000793657500003
- Scopus ID
- 2-s2.0-85127700659
- Other Identifier
- 991021889906604721
UN Sustainable Development Goals (SDGs)
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InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Chemistry, Physical
- Materials Science, Multidisciplinary
- Nanoscience & Nanotechnology
- Physics, Applied