Juhwan Noh

Apr 30, 2024 | pubs.acs.org | Shuan Chen |Juhwan Noh |Jidon Jang |SeongMin Kim

ConspectusThe field of chemical research boasts a long history of developing software to automate synthesis planning and reaction prediction. Early software relied heavily on expert systems, requiring significant effort to encode vast amounts of synthesis knowledge into a computer-readable format. However, recent advancements in deep learning have shifted the focus toward AI models, offering improved prediction capabilities.

Apr 30, 2024 | dx.doi.org | Shuan Chen |Juhwan Noh |Jidon Jang |SeongMin Kim

ConspectusThe field of chemical research boasts a long history of developing software to automate synthesis planning and reaction prediction. Early software relied heavily on expert systems, requiring significant effort to encode vast amounts of synthesis knowledge into a computer-readable format. However, recent advancements in deep learning have shifted the focus toward AI models, offering improved prediction capabilities.

Dec 14, 2023 | pubs.rsc.org | SeongMin Kim |Juhwan Noh

Predicting synthesis recipes of inorganic crystal materials using elementwise template formulation† While advances in computational techniques have accelerated virtual materials design, the actual synthesis of predicted candidate materials is still an expensive and slow process.

Dec 8, 2023 | pubs.rsc.org | SeongMin Kim |Juhwan Noh |Geun Ho Gu |Shuan Chen

Predicting Synthesis Recipes of Inorganic Crystal Materials using Elementwise Template Formulation While the advances in computational techniques accelerated the virtual materials design, the actual synthesis of predicted candidate materials still remain as an expensive and slow process.