Novel molecules and materials are traditionally developed based on experimental screening or a priori theoretical calculations relating molecular structure with desired properties. However, these approaches are expensive, and shortening the timescale of discovery requires a rapid screening methodology. This proposal will develop an artificial intelligence (AI) framework to rapidly explore the chemical space and discover molecules with properties needed for a specific application requirement. To this end, this study has two main objectives. The first is to develop quantitative structure property relationship (QSPR) models to predict how molecular structure affects molecular properties. The second objective is to use variational autoencoders and adversarial training to design new molecules with desired properties.
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