TY - JOUR
T1 - Automated Design of Li+-Conducting Polymer by Quantum-Inspired Annealing
AU - Hatakeyama-Sato, Kan
AU - Adachi, Hiroki
AU - Umeki, Momoka
AU - Kashikawa, Takahiro
AU - Kimura, Koichi
AU - Oyaizu, Kenichi
N1 - Funding Information:
This work was partially supported by the Grants‐in‐Aid for Scientific Research (Nos. 21H04695, 18H05515, 22H04623, and 21H02017) from MEXT, Japan. The work was also partially supported by the JST FOREST Program (Grant Number JPMJFR213V, Japan) and the Research Institute for Science and Engineering, Waseda University. Original data for Figures 2 and 4 are provided in Supporting Information.
Funding Information:
This work was partially supported by the Grants-in-Aid for Scientific Research (Nos. 21H04695, 18H05515, 22H04623, and 21H02017) from MEXT, Japan. The work was also partially supported by the JST FOREST Program (Grant Number JPMJFR213V, Japan) and the Research Institute for Science and Engineering, Waseda University. Original data for Figures 2 and 4 are provided in Supporting Information.
Publisher Copyright:
© 2022 Wiley-VCH GmbH.
PY - 2022/10
Y1 - 2022/10
N2 - Automated molecule design by computers is an essential topic in materials informatics. Still, generating practical structures is not easy because of the difficulty in treating material stability, synthetic difficulty, mechanical properties, and other miscellaneous parameters, often leading to the generation of junk molecules. The problem is tackled by introducing supervised/unsupervised machine learning and quantum-inspired annealing. This autonomous molecular design system can help experimental researchers discover practical materials more efficiently. Like the human design process, new molecules are explored based on knowledge of existing compounds. A new solid-state polymer electrolyte for lithium-ion batteries is designed and synthesized, giving a promising room temperature conductivity of 10−5 S cm−1 with reasonable thermal, chemical, and mechanical properties.
AB - Automated molecule design by computers is an essential topic in materials informatics. Still, generating practical structures is not easy because of the difficulty in treating material stability, synthetic difficulty, mechanical properties, and other miscellaneous parameters, often leading to the generation of junk molecules. The problem is tackled by introducing supervised/unsupervised machine learning and quantum-inspired annealing. This autonomous molecular design system can help experimental researchers discover practical materials more efficiently. Like the human design process, new molecules are explored based on knowledge of existing compounds. A new solid-state polymer electrolyte for lithium-ion batteries is designed and synthesized, giving a promising room temperature conductivity of 10−5 S cm−1 with reasonable thermal, chemical, and mechanical properties.
KW - materials informatics
KW - quantum-annealing
KW - solid polymer electrolytes
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U2 - 10.1002/marc.202200385
DO - 10.1002/marc.202200385
M3 - Article
C2 - 35759445
AN - SCOPUS:85133529147
SN - 1022-1336
VL - 43
JO - Macromolecular rapid communications
JF - Macromolecular rapid communications
IS - 20
M1 - 2200385
ER -