‘CrystalGPT’ set to enhance how chemists design crystals in silico
Model for predicting molecular crystal properties is readily adaptable to specific tasks, even with limited data
Researchers in the UK have developed a transformer-based model for quickly predicting the physical properties of molecular crystals. Called Molecular Crystal Representation from Transformers, or MCRT for short, the team behind the model liken it to ChatGPT. Andrew Cooper, from the University of Liverpool, who was part of the team, says their ‘“Crystal GPT” [was] trained on the largest available database of organic crystals,’ and ‘has learned the most distinctive patterns within these crystals, using multiple representations such as symmetry and crystal density, and how these patterns relate to practical properties’.