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Abstract The metallic tin (Sn) anode is a promising candidate for next‐generation lithium‐ion batteries (LIBs) due to its high theoretical capacity and electrical conductivity. However, Sn suffers from severe mechanical degradation caused by large volume changes during lithiation/delithiation, which leads to a rapid capacity decay for LIBs application. Herein, a Cu–Sn (e.g., Cu3Sn) intermetallic coating layer (ICL) is rationally designed to stabilize Sn through a structural reconstruction mechanism. The low activity of the Cu–Sn ICL against lithiation/delithiation enables the gradual separation of the metallic Cu phase from the Cu–Sn ICL, which provides a regulatable and appropriate distribution of Cu to buffer volume change of Sn anode. Concurrently, the homogeneous distribution of the separated Sn together with Cu promotes uniform lithiation/delithiation, mitigating the internal stress. In addition, the residual rigid Cu–Sn intermetallic shows terrific mechanical integrity that resists the plastic deformation during the lithiation/delithiation. As a result, the Sn anode enhanced by the Cu–Sn ICL shows a significant improvement in cycling stability with a dramatically reduced capacity decay rate of 0.03% per cycle for 1000 cycles. The structural reconstruction mechanism in this work shines a light on new materials and structural design that can stabilize high‐performance and high‐volume‐change electrodes for rechargeable batteries and beyond.
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Machine learning-assisted high-throughput exploration of interface energy space in multi-phase-field model with CALPHAD potential
Abstract Computational methods are increasingly being incorporated into the exploitation of microstructure–property relationships for microstructure-sensitive design of materials. In the present work, we propose non-intrusive materials informatics methods for the high-throughput exploration and analysis of a synthetic microstructure space using a machine learning-reinforced multi-phase-field modeling scheme. We specifically study the interface energy space as one of the most uncertain inputs in phase-field modeling and its impact on the shape and contact angle of a growing phase during heterogeneous solidification of secondary phase between solid and liquid phases. We evaluate and discuss methods for the study of sensitivity and propagation of uncertainty in these input parameters as reflected on the shape of the Cu6Sn5intermetallic during growth over the Cu substrate inside the liquid Sn solder due to uncertain interface energies. The sensitivity results rankσSI,σIL, andσIL, respectively, as the most influential parameters on the shape of the intermetallic. Furthermore, we use variational autoencoder, a deep generative neural network method, and label spreading, a semi-supervised machine learning method for establishing correlations between inputs of outputs of the computational model. We clustered the microstructures into three categories (“wetting”, “dewetting”, and “invariant”) using the label spreading method and compared it with the trend observed in the Young-Laplace equation. On the other hand, a structure map in the interface energy space is developed that showsσSIandσSLalter the shape of the intermetallic synchronously where an increase in the latter and decrease in the former changes the shape from dewetting structures to wetting structures. The study shows that the machine learning-reinforced phase-field method is a convenient approach to analyze microstructure design space in the framework of the ICME.
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- Award ID(s):
- 2001333
- PAR ID:
- 10361289
- Publisher / Repository:
- Springer Science + Business Media
- Date Published:
- Journal Name:
- Materials Theory
- Volume:
- 6
- Issue:
- 1
- ISSN:
- 2509-8012
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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