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Leveraging Machine Learning to Find Promising Compositions for Sodium-Ion Batteries
2024.11.05 Tuesday
Leveraging Machine Learning to Find Promising Compositions for Sodium-Ion Batteries
Researchers optimize the composition of a multi-element transition metal oxide to achieve exceptional energy density in sodium-ion batteries
Energy storage is an essential part of many rapidly growing sustainable technologies, including electric cars and renewable energy generation. Although lithium-ion batteries (LIBs) dominate the current market, lithium is a relatively scarce and expensive element, creating both economic and supply stability challenges. Accordingly, researchers all over the world are experimenting with new types of batteries made from more abundant materials.
Sodium-ion (Na-ion) batteries which use sodium ions as energy carriers present a promising alternative to LIBs owing to the abundance of sodium, their higher safety, and potentially lower cost. In particular, sodium-containing transition-metal layered oxides (NaMeO₂
are powerful materials for the positive electrode of Na-ion batteries, offering exceptional energy density and capacity. However, for multi-element layered oxides composed of several transition metals, the sheer number of possible combinations makes finding the optimal composition both complex and time-consuming. Even minor changes in the selection and proportion of transition metals can bring about marked changes in crystal morphology and affect battery performance.
Now, in a recent study, a research team led by Professor Shinichi Komaba, along with Ms. Saaya Sekine and Dr. Tomooki Hosaka from Tokyo University of Science (TUS), Japan, and from Chalmers University of Technology, and Professor Masanobu Nakayama from Nagoya Institute of Technology, leveraged machine learning to streamline the search for promising compositions. The findings of their study were received on September 05, 2024, with uncorrected proofs and published online in the Journal of Materials Chemistry A on November 06, 2024, after proofreading. This research study is supported by funding agencies JST-CREST, DX-GEM, and JST-GteX.
The team sought to automate the screening of elemental compositions in various NaMeO2 O3-type materials. To this end, they first assembled a database of 100 samples from O3-type sodium half-cells with 68 different compositions, gathered over the course of 11 years by Komaba's group. "The database included the composition of NaMeO2 samples, with Me being a transition metal like Mn, Ti, Zn, Ni, Zn, Fe, and Sn, among others, as well as the upper and lower voltage limits of charge-discharge tests, initial discharge capacity, average discharge voltage, and capacity retention after 20 cycles," explains Komaba.
…
http://dx.doi.org/10.1039/D4TA04809ALeveraging Machine Learning to Find Promising Compositions for Sodium-Ion Batteries
Researchers optimize the composition of a multi-element transition metal oxide to achieve exceptional energy density in sodium-ion batteries
Energy storage is an essential part of many rapidly growing sustainable technologies, including electric cars and renewable energy generation. Although lithium-ion batteries (LIBs) dominate the current market, lithium is a relatively scarce and expensive element, creating both economic and supply stability challenges. Accordingly, researchers all over the world are experimenting with new types of batteries made from more abundant materials.
Sodium-ion (Na-ion) batteries which use sodium ions as energy carriers present a promising alternative to LIBs owing to the abundance of sodium, their higher safety, and potentially lower cost. In particular, sodium-containing transition-metal layered oxides (NaMeO₂
are powerful materials for the positive electrode of Na-ion batteries, offering exceptional energy density and capacity. However, for multi-element layered oxides composed of several transition metals, the sheer number of possible combinations makes finding the optimal composition both complex and time-consuming. Even minor changes in the selection and proportion of transition metals can bring about marked changes in crystal morphology and affect battery performance.
Now, in a recent study, a research team led by Professor Shinichi Komaba, along with Ms. Saaya Sekine and Dr. Tomooki Hosaka from Tokyo University of Science (TUS), Japan, and from Chalmers University of Technology, and Professor Masanobu Nakayama from Nagoya Institute of Technology, leveraged machine learning to streamline the search for promising compositions. The findings of their study were received on September 05, 2024, with uncorrected proofs and published online in the Journal of Materials Chemistry A on November 06, 2024, after proofreading. This research study is supported by funding agencies JST-CREST, DX-GEM, and JST-GteX.
The team sought to automate the screening of elemental compositions in various NaMeO2 O3-type materials. To this end, they first assembled a database of 100 samples from O3-type sodium half-cells with 68 different compositions, gathered over the course of 11 years by Komaba's group. "The database included the composition of NaMeO2 samples, with Me being a transition metal like Mn, Ti, Zn, Ni, Zn, Fe, and Sn, among others, as well as the upper and lower voltage limits of charge-discharge tests, initial discharge capacity, average discharge voltage, and capacity retention after 20 cycles," explains Komaba.
…