Extracting higher-conductivity designs for solid polymer electrolytes by quantum-inspired annealing

Hatakeyama-Sato, Kan; Uchima, Yasuei; Kashikawa, Takahiro; Kimura, Koichi; Oyaizu, Kenichi

doi:10.1039/d3ra01982a

Cited by 6 publications

(3 citation statements)

References 48 publications

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“…Some works have already shown the possibilities of using AI for polymer electrolytes. In the past year, different models such as a chemistry-informed neural network 88 and a data trend analysis system for materials using quantum-inspired annealing 89 have been applied to accurately predict ionic conductivity in solid polymer electrolytes. Using the trained model, ionic conductivity values were predicted for several thousand candidate solid polymer electrolytes.…”

Section: Future Directionsmentioning

confidence: 99%

Current Trends and Perspectives of Polymers in Batteries

Mecerreyes,

Casado,

Villaluenga

et al. 2024

Macromolecules

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This Perspective aims to present the current status and future opportunities for polymer science in battery technologies. Polymers play a crucial role in improving the performance of the ubiquitous lithium ion battery. But they will be even more important for the development of sustainable and versatile post-lithium battery technologies, in particular solid-state batteries. In this article, we identify the trends in the design and development of polymers for battery applications including binders for electrodes, porous separators, solid electrolytes, or redox-active electrode materials. These trends will be illustrated using a selection of recent polymer developments including new ionic polymers, biobased polymers, self-healing polymers, mixed-ionic electronic conducting polymers, inorganic–polymer composites, or redox polymers to give some examples. Finally, the future needs, opportunities, and directions of the field will be highlighted.

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Section: Future Directionsmentioning

confidence: 99%

Current Trends and Perspectives of Polymers in Batteries

Mecerreyes,

Casado,

Villaluenga

et al. 2024

Macromolecules

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“…Furthermore, based on the existing simulated annealing algorithm, DA further includes the parallel search technique, inspired by quantum nature, to increase the state change accepting probabilities. , These techniques make DA promising QUBO solvers for near-term applications. In recent years, K. Hatakeyama-Sato et al have applied DA in materials science and achieved big successes, such as Li + -conducting polymer and solid polymer electrolytes …”

Section: Introductionmentioning

confidence: 99%

Efficient Exploration of Phenol Derivatives Using QUBO Solvers with Group Contribution-Based Approaches

Cho,

Su,

et al. 2024

Ind. Eng. Chem. Res.

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Molecule screening from a vast number of possible compounds is a challenging task. The emergence of quadratic unconstrained binary optimization (QUBO) solvers provides alternatives to address this issue. We propose a process for screening molecules by integrating QUBO solvers and density functional theory (DFT) calculations. As a proof-of-concept work, we map the problem of screening phenolic inhibitors onto the QUBO model. We approximate the bond dissociation energy (BDE) of the −OH bond, an indicator of good polymeric inhibitors, into the QUBO model by modifying the group contribution method (GCM) with the aid of DFT calculations. We demonstrate a strong correlation between this QUBO model and the data from DFT, with the correlation coefficient and Spearman's coefficient of 0.82 and 0.86, respectively, when tested on the 85 given molecules. This mapping allows us to identify the candidates through the QUBO solver, whose BDEs are validated through DFT calculations, as well. Our work provides a promising direction for incorporating the GCM into QUBO solvers to tackle the molecule screening problems.

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“…[ 25 ] FMQA facilitates faster candidate selection by using binary representation for the material search space during the optimization process. Black box optimization based on Ising machines has been used to design complex materials such as metamaterials, [ 21 ] materials with a layered photonic structure, [ 26 ] materials with different chemical structures, [ 27 ] printed circuit boards, [ 28 ] and photonic crystals. [ 29 ] However, FMQA is not suitable for optimization problems where the candidate materials represented by descriptors are listed in advance.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Core–Shell Nanoparticles Using a Combination of Machine Learning and Ising Machine

Urushihara,

Karube,

Yamaguchi

et al. 2023

Advanced Photonics Research

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Machine‐learning‐based optimization techniques are widely used for designing complex materials. However, an efficient search for the complex systems, where a combinatorial explosion occurs in the materials search space, is still challenging. Core–shell nanoparticles (CSNPs) are an example of a complex system with a high degree of freedom owing to their complicated structure and multiple constituent materials. In this study, a new black box optimization technique is developed. In this method, the structure of the CSNPs is optimized using an Ising machine and their constituent materials are selected using Bayesian optimization with the optical properties of the materials as the “descriptors”. Aiming for applications to i‐line photolithography, the authors search for CSNPs that are transparent to ultraviolet light of wavelength 355–375 nm and opaque to visible light of wavelength 400–830 nm. The transmittance spectra of the nanoparticles are obtained using a Mie theory calculator. The proposed nanoparticles with the best optical properties have a multilayered structure with a radius of approximately 40 nm and an outer shell composed of either Mg or Pb. The results indicate that a combination of various optimization techniques is more efficient in discovering the better complex materials.

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Extracting higher-conductivity designs for solid polymer electrolytes by quantum-inspired annealing

Abstract: A quantum-inspired annealing system with a hybrid algorithm accelerates functional material discovery, shown by high-conductivity polymer electrolytes.

Cited by 6 publications

References 48 publications

Current Trends and Perspectives of Polymers in Batteries

Current Trends and Perspectives of Polymers in Batteries

Efficient Exploration of Phenol Derivatives Using QUBO Solvers with Group Contribution-Based Approaches

Optimization of Core–Shell Nanoparticles Using a Combination of Machine Learning and Ising Machine

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