With climate change posing an unprecedented global challenge, the demand for environmentally friendly solvents in green chemical processes and carbon dioxide capture has surged. Ionic liquids (ILs) have emerged as promising "designer solvents" due to their negligible volatility, broad liquid temperature range, and exceptional thermal stability. However, the immense chemical space of ILs—with theoretically up to 10¹⁸ possible cation-anion combinations—has created a critical bottleneck in efficient screening and design. Traditional experimental methods are costly and time-consuming, while theoretical calculations like molecular dynamics and quantum chemistry remain computationally prohibitive for large-scale screening. This urgent need for accelerated discovery has set the stage for a transformative technological leap.

Since 2021, Korean researchers have been providing a simple software development framework to users with relatively limited AI expertise in industrial fields such as factories, medical, and shipbuilding, providing them with a significant boost. ETRI announced that it has released the core technology of MLOps tool, which automatically generates neural networks based on no-code and automates the deployment process, as open source on GitHub.

Accurately predicting complex agronomic traits remains a major bottleneck in crop breeding. This study demonstrates how optimized genomic prediction models can reliably forecast flowering time, yield components, and oil content in rapeseed using genome-wide genetic information.