Optimal high-throughput virtual screening pipeline for efficient selection of redox-active organic materials

As global interest in renewable energy continues to increase, there has been a pressing need for developing novel energy storage devices based on organic electrode materials that can overcome the shortcomings of the current lithium -ion batteries. One critical challenge for this quest is to find materials whose redox potential (RP) meets specific design targets. In this study, we propose a compu-tational framework for addressing this challenge through the effective design and optimal operation of a high-throughput virtual screening (HTVS) pipeline that enables rapid screening of organic materials that satisfy the desired criteria. Starting from a high-fidelity model for estimating the RP of a given material, we show how a set of surrogate models with different accuracy and complexity may be designed to construct a highly accurate and efficient HTVS pipeline. We demonstrate that the proposed HTVS pipeline construction and operation strate-gies substantially enhance the overall screening throughput.

Automated summary

As the global interest in renewable energy grows, there is a need for new energy storage devices based on organic electrode materials to overcome the limitations of current lithium-ion batteries. This study proposes a computational framework for designing and optimizing a high-throughput virtual screening pipeline to quickly identify organic materials that meet specific design criteria. By constructing a set of surrogate models with varying accuracy and complexity, starting from a high-fidelity model, the research demonstrates that the proposed pipeline significantly improves screening efficiency.