Machine Learning Enabled Computational Screening of Inorganic Solid Electrolytes for Suppression of Dendrite Formation in Lithium Metal Anodes
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Summary
A computational screening of over 12 000 inorganic solids based on their ability to suppress dendrite initiation in contact with Li metal anode finds that material stiffness is found to increase with an increase in mass density and ratio of Li and sublattice bond ionicity, and decrease with increase in volume per atom and subLattice electronegativity.
- Type
- article
- Published
- 2018-04-12
- Cited by
- 239
- References
- 97
- Access
- Open access
- OpenAlex
- https://openalex.org/W2797406032
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:52119322
Keywords
Anode, Dendrite (mathematics), Isotropy, Electrolyte, Metal
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- Quantifying the Search for Solid Li-Ion Electrolyte Materials by Anion: A Data-Driven Perspective
- Computational screening of electrolyte materials: status quo and open problems
- Improved Chemical Prediction from Scarce Data Sets via Latent Space Enrichment.
- A perspective on inverse design of battery interphases using multi-scale modelling, experiments and generative deep learning
- Recent advances in understanding dendrite growth on alkali metal anodes
- Dendrite suppression of metal electrodeposition with liquid crystalline electrolytes
- Lattice Convolutional Neural Network Modeling of Adsorbate Coverage Effects
- Predicting charge density distribution of materials using a local-environment-based graph convolutional network
- Materials Science in the AI age: high-throughput library generation, machine learning and a pathway from correlations to the underpinning physics
- Neural network-based transductive regression model
- Interfacial Electronic Properties Dictate Li Dendrite Growth in Solid Electrolytes
- Fundamentals of inorganic solid-state electrolytes for batteries
- Flexible lignin carbon membranes with surface ozonolysis to host lean lithium metal anodes for nickel-rich layered oxide batteries
- A high throughput molecular screening for organic electronics via machine learning: present status and perspective
- Topological analysis of procrystal electron densities as a tool for computational modeling of solid electrolytes: A case study of known and promising potassium conductors
- Machine learning prediction of coordination energies for alkali group elements in battery electrolyte solvents.
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