Generative Models as an Emerging Paradigm in the Chemical Sciences

Traditional computational approaches to design chemical species are limited by the need to compute properties for a vast number of candidates, e.g., by discriminative modeling. Therefore, inverse design methods aim to start from the desired property and optimize a corresponding chemical structure. From a machine learning viewpoint, the inverse design problem can be addressed through so-called generative modeling. Mathematically, discriminative models are defined by learning the probability distribution function of properties given the molecular or material structure. In contrast, a generative model seeks to exploit the joint probability of a chemical species with target characteristics. The overarching idea of…

• NS
  National Science Foundation

  Awards: CHE-2154447, 2154447
• MU
  Multidisciplinary University Research Initiative

  Award: N00014-21-1-2476
• OO
  Office of Naval Research

  Awards: N00014-21-1-2476, N00014
• DO
  Division of Chemistry

  Awards: CHE-2154447, 2154447