ChemReservoir -- An Open-Source Framework for Chemically-Inspired Reservoir Computing

TL;DR

ChemReservoir is an open-source framework that enables the construction and analysis of chemically-inspired reservoir computing systems, improving reproducibility and flexibility over previous DNA-focused tools.

Contribution

It introduces a general, open-source framework for chemically-inspired reservoirs, addressing limitations of prior DNA-centric software and enhancing testing and evaluation capabilities.

Findings

Demonstrated stable performance across various reservoir topologies

Effective in memory capacity tasks

Addresses reproducibility issues in chemical reservoir modeling

Abstract

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.