A new tool developed by scientists at Georgia Tech captures patterns and relationships and learns the grammar and syntax that occur at the atomic and higher levels in polymer structures.
Because the combinations are essentially endless, figuring out which combinations of materials will make the most effective polymers is a monumental and time-consuming task. To help with this work, researchers at Georgia Tech have developed a machine-learning model that could revolutionize how scientists and manufacturers virtually search the chemical space to identify and develop these all-important polymers. The U.S. National Science Foundation-supported team published its findings in Nature Communications.
The work was conceived and guided by engineer Rampi Ramprasad at Georgia Tech. The new tool aims to overcome the challenges of searching the large chemical space of polymers. Trained on a massive dataset of 80 million polymer chemical structures, polyBERT, as it's called, has become an expert in understanding the language of polymers.