Researchers have used machine learning to create a model that simulates reactive processes in organic materials and conditions. Researchers from Carnegie Mellon University and Los Alamos National ...

Atom-to-atom mapping (AAM) plays a crucial role in preparing reaction data by identifying the one-to-one mapping between reactant atoms and product atoms. High-quality AAM allows fast recognition of ...

There are few problems now that AI and machine learning cannot help overcome. Researchers from the Yokohama National University are using this modern advantage to resolve what conventional methods ...

Despite the significant potential of generative models, low synthesizability of many generated molecules limits their real-world applications. In response to this issue, we develop ClickGen, a deep ...

Researchers developed a machine-learning model that can predict the structures of transition states of chemical reactions in less than a second, with high accuracy. Their model could make it easier ...

Drug discovery is like molecular Tetris. Chemists snap atoms together, adjusting the pieces until everything fits and suddenly, a molecule makes a promising new medicine. Normally, creating better ...

Researchers from Carnegie Mellon University and Los Alamos National Laboratory have used machine learning to create a model that can simulate reactive processes in a diverse set of organic materials ...

(Nanowerk News) Researchers from Carnegie Mellon University and Los Alamos National Laboratory have used machine learning to create a model that can simulate reactive processes in a diverse set of ...