Artificial intelligence driven in-silico discovery of novel organic lithium-ion battery cathodes
Abstract
Organic electrode materials (OEMs) combine key sustainability and versatility properties with the potential to enable the realisation of the next generation of truly green battery technologies. However, for OEMs to become a competitive alternative, challenging issues related to energy density, rate capability and cycling stability need to be overcome. In this work, we have developed and applied an alternative yet systematic methodology to accelerate the discovery of suitable cathode-active OEMs by interplaying artificial intelligence (AI) and quantum mechanics. This AI-kernel has allowed a high-throughput screening of a huge library of organic molecules, leading to the discovery of 459 novel promising OEMs with candidates offering the potential to achieve theoretical energy densities superior to 1000 W h kg−1. Moreover, the machinery accurately identified common molecular functionalities that lead to such higher-voltage electrodes and pointed out an interesting donor-accepter-like effect that may drive the future design of cathode-active OEMs.
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