Machine Learning-Aided Discovery of Superionic Solid-State Electrolyte for Li-Ion Batteries

Kang, Seungpyo; Kim, Min‐Seon; Min, Kyoungmin

doi:10.48550/arxiv.2202.06763

Cited by 5 publications

(6 citation statements)

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“…We further classified superionic conductors (decision boundary = 10 −4 S/cm) using an ML model developed in our previous study to find candidates that satisfy critical conditions: high mechanical properties and ionic conductivity. 42 Training Database Construction. As discussed previously, the database used in this study was derived from our recent study.…”

Section: ■ Methodsmentioning

confidence: 99%

“…For this reason, we predict the ionic conductivity of garnettype SSE candidates with a new composition using a previously developed ML-surrogate model. 42 For the initial database, the ionic conductivities and crystal system of inorganic SSEs were obtained from previous references, and relevant information can be found in ref 42. The surrogate model was then constructed by ensembling the RF and LGBM models.…”

Section: ■ Methodsmentioning

confidence: 99%

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Accelerated Discovery of Novel Garnet-Type Solid-State Electrolyte Candidates via Machine Learning

Sun

Kang

Kim

et al. 2023

ACS Appl. Mater. Interfaces

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All-solid-state batteries (ASSBs) have attracted considerable attention because of their higher energy density and stability than conventional lithium-ion batteries (LIBs). For the development of promising ASSBs, solid-state electrolytes (SSEs) are essential to achieve structural integrity. Thus, in this study, a machine-learning-based surrogate model was developed to search for ideal garnet-type SSE candidates. The well-known Li 7 La 3 Zr 2 O 12 structure was used as a base material, and 73 chemical elements were substituted on La and Zr sites, leading to 5329 potential structures. First, the elasticity database and machine learning descriptors were adopted from previous studies. Subsequently, the machine-learning-based surrogate model was applied to predict the elastic properties of potential SSE materials, followed by first-principles calculations for validation. Furthermore, the active learning process demonstrated that it can effectively decrease prediction uncertainty. Finally, the ionic conductivity of the mechanically superior materials was predicted to suggest optimal SSE candidates. Then, ab initio molecular dynamics simulations are followed for confirmation of diffusion behavior for materials classified as superionic; 10 new tetragonal-phase garnet SSEs are verified with superior mechanical and ionic conductivity properties. We believe that the current model and the constructed database will become a cornerstone for the development of next-generation SSE materials.

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Section: ■ Methodsmentioning

confidence: 99%

Section: ■ Methodsmentioning

confidence: 99%

Accelerated Discovery of Novel Garnet-Type Solid-State Electrolyte Candidates via Machine Learning

Sun

Kang

Kim

et al. 2023

ACS Appl. Mater. Interfaces

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“…On the other hand, in combination with DFT and ML, it is possible to investigate the extensive exploration space through employing high-throughput computation for the database construction and surrogate model for rapid inference, thereby accelerating the discovery of promising materials. Thus, in the research field of rechargeable batteries, various studies combining ML and DFT are actively progressing, achieving remarkable outcomes: (1) solid-state electrolyte screening, − (2) cathode development, , and (3) anode design are some representative examples.…”

Section: Introductionmentioning

confidence: 99%

Machine-Learning-Driven High-Throughput Screening for High-Energy Density and Stable NASICON Cathodes

Jeong,

Kim,

Sun

et al. 2024

ACS Appl. Mater. Interfaces

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The Na super ionic conductor (NASICON), which has outstanding structural stability and a high operating voltage, is an appealing material for overcoming the limits of low specific energy and larger volume distortion of sodium-ion batteries. In this study, to discover ideal NASICON cathode materials, a screening platform based on density functional theory (DFT) calculations and machine learning (ML) is developed. A training database was generated utilizing the previous 124 545 electrode databases, and a test set of 3126 potential NASICON structures [Na x M y M′ 1−y (PO 4 ) 3 ] with 27 dopants at the metal site and 6 dopants at the polyanion central site was constructed. The developed ML surrogate model identifies 796 materials that satisfy the following criteria: formation energy of <0.0 eV/atom, energy above hull of ≤0.025 eV/atom, volume change of ≤4%, and theoretical capacity of ≥50 mAh/g. The thermodynamically stable configurations of doped NASICON structures were then selected using machine learning interatomic potential (MLIP), enabling rapid consideration of various dopant site configurations. DFT calculations are followed on 796 screened materials to obtain energy density, average voltage, and volume change. Finally, 50 candidates with an average voltage of ≥3.5 V are identified. The suggested platform accelerates the exploration for optimal NASICON materials by narrowing the focus on materials with desired properties, saving considerable resources.

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“…Existing, large databases such as the Materials Project, Open Quantum Materials Database (OQMD), and the Automatic FLOW for Materials Discovery (AFLOW) contain between tens of thousands to millions of entries from first-principles calculations. Many independent screening efforts have searched the Materials Project Database for Li-based SSE, ,,,− electrode material, and electrode coating candidates. ,, Others screened the ICSD, a large database containing experimentally known crystal structures. ,,,− …”

Section: Introductionmentioning

confidence: 99%

High-Throughput Screening of Li Solid-State Electrolytes with Bond Valence Methods and Machine Learning

Xie,

Honrao,

Lawson

2024

Chem. Mater.

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Li-based solid-state electrolyte materials enable safer, all-solid-state batteries, but the computational search for candidates with favorable stability and high Li-ion conductivity is challenging due to the size of the search space and the cost of evaluating transport properties with ab initio methods. We present a highthroughput screening approach for identifying promising materials using a combination of bond-valence methods and graph neural networks. An ablation study involving geometric and bond-valence quantities reveals their relative importance in the training of graph neural networks, providing insight for future modeling of ionic conductivity in Li SSE. We identify 329 candidates with good stability and ionic conductivity, including 28 stable against Li metal. Furthermore, we combine the ML-accelerated screening procedure with an isovalent substitution scheme to generate and screen additional candidates beyond existing databases, identifying an additional 239 candidate materials for battery applications.

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Machine Learning-Aided Discovery of Superionic Solid-State Electrolyte for Li-Ion Batteries

Cited by 5 publications

References 0 publications

Accelerated Discovery of Novel Garnet-Type Solid-State Electrolyte Candidates via Machine Learning

Accelerated Discovery of Novel Garnet-Type Solid-State Electrolyte Candidates via Machine Learning

Machine-Learning-Driven High-Throughput Screening for High-Energy Density and Stable NASICON Cathodes

High-Throughput Screening of Li Solid-State Electrolytes with Bond Valence Methods and Machine Learning

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