ChemReservoir -- An Open-Source Framework for Chemically-Inspired Reservoir Computing
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866913876779466752 |
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| author | Yirik, Mehmet Aziz Andersen, Jakob Lykke Fagerberg, Rolf Merkle, Daniel |
| author_facet | Yirik, Mehmet Aziz Andersen, Jakob Lykke Fagerberg, Rolf Merkle, Daniel |
| contents | Reservoir computing is a type of a recurrent neural network, mapping the inputs into higher dimensional space using fixed and nonlinear dynamical systems, called reservoirs. In the literature, there are various types of reservoirs ranging from in-silico to in-vitro. In cheminformatics, previous studies contributed to the field by developing simulation-based chemically inspired in-silico reservoir models. Yahiro used a DNA-based chemical reaction network as its reservoir and Nguyen developed a DNA chemistry-inspired tool based on Gillespie algorithm. However, these software tools were designed mainly with the focus on DNA chemistry and their maintenance status has limited their current usability. Due to these limitations, there was a need for a proper open-source tool. This study introduces ChemReservoir, an open-source framework for chemically-inspired reservoir computing. In contrast to the former studies focused on DNA-chemistry, ChemReservoir is a general framework for the construction and analysis of chemically-inspired reservoirs, which also addresses the limitations in these previous studies by ensuring enhanced testing, evaluation, and reproducibility. The tool was evaluated using various cycle-based reservoir topologies and demonstrated stable performance across a range of configurations in memory capacity tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_04249 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ChemReservoir -- An Open-Source Framework for Chemically-Inspired Reservoir Computing Yirik, Mehmet Aziz Andersen, Jakob Lykke Fagerberg, Rolf Merkle, Daniel Computational Engineering, Finance, and Science Emerging Technologies Machine Learning Reservoir computing is a type of a recurrent neural network, mapping the inputs into higher dimensional space using fixed and nonlinear dynamical systems, called reservoirs. In the literature, there are various types of reservoirs ranging from in-silico to in-vitro. In cheminformatics, previous studies contributed to the field by developing simulation-based chemically inspired in-silico reservoir models. Yahiro used a DNA-based chemical reaction network as its reservoir and Nguyen developed a DNA chemistry-inspired tool based on Gillespie algorithm. However, these software tools were designed mainly with the focus on DNA chemistry and their maintenance status has limited their current usability. Due to these limitations, there was a need for a proper open-source tool. This study introduces ChemReservoir, an open-source framework for chemically-inspired reservoir computing. In contrast to the former studies focused on DNA-chemistry, ChemReservoir is a general framework for the construction and analysis of chemically-inspired reservoirs, which also addresses the limitations in these previous studies by ensuring enhanced testing, evaluation, and reproducibility. The tool was evaluated using various cycle-based reservoir topologies and demonstrated stable performance across a range of configurations in memory capacity tasks. |
| title | ChemReservoir -- An Open-Source Framework for Chemically-Inspired Reservoir Computing |
| topic | Computational Engineering, Finance, and Science Emerging Technologies Machine Learning |
| url | https://arxiv.org/abs/2506.04249 |