Structural design of organic battery electrode materials: from DFT to artificial intelligence

Wu, Ting-Ting; Dai, Gao-Le; Xu, Jin-Jia; Cao, Fang; Zhang, Xiao-Hong; Zhao, Yu; Qian, Yu-Min

doi:10.1007/s12598-023-02358-1

Cited by 16 publications

(3 citation statements)

References 148 publications

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“…Introducing heteroatoms into molecular skeletons has been reported as an effective strategy to enhance the discharge voltage as well as the reaction kinetics of organic electrodes. [34][35][36][37] The SC4Q molecule, consisting of sulfonyl functional groups and BQ units, probably exhibits strong electrochemical activity as the cathode for achieving great Li + storage properties. To simulate and verify these properties, the optimized molecular structure (Table S2-S7 †) and frontier molecular orbitals were first calculated through the DFT method.…”

Section: Resultsmentioning

confidence: 99%

Synthesis, characterization and electrochemical lithium-ion storage properties of sulfonylcalix[4]quinone

Zhang,

Lin,

Huang

2023

Inorg. Chem. Front.

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Section: Resultsmentioning

confidence: 99%

Synthesis, characterization and electrochemical lithium-ion storage properties of sulfonylcalix[4]quinone

Zhang,

Lin,

Huang

2023

Inorg. Chem. Front.

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“…Machine learning (ML) as the core part of artificial intelligence (AI) is regarded as a viable technique in materials science, which can find the statistical law behind high-dimensional data to acquire reliable and repeatable results . Inspiritingly, integrating ML and DFT technologies has become an effective computational method to implement high accuracy analysis the composition–structure–property relationships in electrode materials. − Combining computational chemistry and AI can avoid the traditional “trial-and-error” processes and significantly accelerate the development of material systems.…”

Section: Characterization Methodology Of Carbonyl Speciesmentioning

confidence: 99%

Carbonyl Chemistry for Advanced Electrochemical Energy Storage Systems

Zou,

Deng,

Silvester

et al. 2024

ACS Nano

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On the basis of the sustainable concept, organic compounds and carbon materials both mainly composed of light C element have been regarded as powerful candidates for advanced electrochemical energy storage (EES) systems, due to theie merits of low cost, eco-friendliness, renewability, and structural versatility. It is investigated that the carbonyl functionality as the most common constituent part serves a crucial role, which manifests respective different mechanisms in the various aspects of EES systems. Notably, a systematical review about the concept and progress for carbonyl chemistry is beneficial for ensuring in-depth comprehending of carbonyl functionality. Hence, a comprehensive review about carbonyl chemistry has been summarized based on state-of-the-art developments. Moreover, the working principles and fundamental properties of the carbonyl unit have been discussed, which has been generalized in three aspects, including redox activity, the interaction effect, and compensation characteristic. Meanwhile, the pivotal characterization technologies have also been illustrated for purposefully studying the related structure, redox mechanism, and electrochemical performance to profitably understand the carbonyl chemistry. Finally, the current challenges and promising directions are concluded, aiming to afford significant guidance for the optimal utilization of carbonyl moiety and propel practicality in EES systems.

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“…Since the early 2010s, the integration of machine learning into electrochemical research has been embarked on in a wide range of fields such as energy generation/storage [ 1 , 2 , 3 , 4 ], bio/chemical analysis [ 5 , 6 , 7 ], fundamental electrochemistry [ 8 , 9 ], and environmental sustainability [ 10 ] ( Figure 1 A). The main goals of machine learning applications are, from a material point of view, to improve the functionality of materials utilized in electrochemical research and, from an analysis point of view, to facilitate data processing and interpretation [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Strategies to Enrich Electrochemical Sensing Data with Analytical Relevance for Machine Learning Applications: A Focused Review

Kang,

Kim,

Kim

et al. 2024

Sensors

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In this review, recent advances regarding the integration of machine learning into electrochemical analysis are overviewed, focusing on the strategies to increase the analytical context of electrochemical data for enhanced machine learning applications. While information-rich electrochemical data offer great potential for machine learning applications, limitations arise when sensors struggle to identify or quantitatively detect target substances in a complex matrix of non-target substances. Advanced machine learning techniques are crucial, but equally important is the development of methods to ensure that electrochemical systems can generate data with reasonable variations across different targets or the different concentrations of a single target. We discuss five strategies developed for building such electrochemical systems, employed in the steps of preparing sensing electrodes, recording signals, and analyzing data. In addition, we explore approaches for acquiring and augmenting the datasets used to train and validate machine learning models. Through these insights, we aim to inspire researchers to fully leverage the potential of machine learning in electroanalytical science.

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Structural design of organic battery electrode materials: from DFT to artificial intelligence

Cited by 16 publications

References 148 publications

Synthesis, characterization and electrochemical lithium-ion storage properties of sulfonylcalix[4]quinone

Synthesis, characterization and electrochemical lithium-ion storage properties of sulfonylcalix[4]quinone

Carbonyl Chemistry for Advanced Electrochemical Energy Storage Systems

Strategies to Enrich Electrochemical Sensing Data with Analytical Relevance for Machine Learning Applications: A Focused Review

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