The machine learning framework learns the language of molecules to generate new ones and predict the properties of materials, paving the way for material and drug discovery.
"Our goal with this project is to use some data-driven methods to speed up the discovery of new molecules, so you can train a model to do the prediction without all of these cost-heavy experiments," said Guo in aTraditional methods rely on ML models acquiring knowledge based on large datasets which aren't domain-specific. This results in the model performing poorly.. Atoms and molecules obey laws or rules of physics that dictate how they interact with each other to form molecules.
The team used reinforcement learning to train the system on the production rules of molecular grammar. They simplified the learning process by breaking the molecular grammar into two components—a general metagrammar and a molecule-specific grammar. Some prior approaches also needed costly pretraining, which their system dodges. Their system performed remarkably nally well at predicting the properties of polymers like glass. These properties are hard to determine experimentally, requiring very high pressures and temperatures.
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