Project 6: Machine learning for supported organic electrode materials

15. December 2021

With the rising demand in alternative sustainable energy sources, diverse electroactive organic molecules arise as attractive electrode materials for rechargeable metal-ion batteries. However, current organic electrode materials generally suffer from low conductivity and poor battery life cycle due to their high solubility in conventional non-aqueous electrolytes. These issues can be simultaneously addressed by introducing conductive supports for these electroactive molecules.

In this project we aim to develop a facile screening and design protocol for the graphene-based nanocomposites as organic electrode materials. Vast chemical space of nanocomposites, arising from the range of possible electroactive organic molecules and a multitude of structural variations of the graphene-based supports, will be subject to automated exploration with machine learning techniques to alleviate the need in high-level quantum-chemical computations of the basis properties beyond the initial training set.

Developed approach for automated screening of the graphene-based nanocomposites will facilitate discovery and selection of new electrode materials for rechargeable batteries beyond the current trial-and-error approach. This framework can be further transferred to other applications of small molecule interactions with the graphene-based materials, e.g., in sensing and catalysis.

Key components of supported organic electrode materials that will be mixed-and-matched using machine learning.

Team

Dr. Ganna (Anya) Gryn’ova

Principal Investigator (HITS)

Phone: +49 6221 533 241

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Prof. Dr. Marcus Elstner

Principal Investigator (Karlsruhe Institute of Technology)

Phone: +49 721 60845705

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T.T.-Prof. Dr. Pascal Friederich

Principal Investigator (Karlsruhe Institute of Technology)

Phone: +49 721 60844764

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