Stoichiometry Representation Learning with Polymorphic Crystal
  Structures
Namkyeong Lee Heewoong Noh Gyoung S. Na Tianfan Fu Jimeng Sun Chanyoung Park
Despite the recent success of machine learning (ML) in materials science, its
success heavily relies on the structural description of crystal, which is
itself computationally demanding and occasionally unattainable. Stoichiometry
descriptors can be an alternative approach, which reveals the ratio between
elements involved to form a certain compound without any structural
information. However, it is not trivial to learn the representations of
stoichiometry due to the nature of materials science called polymorphism, i.e.,
a single stoichiometry can exist in multiple structural forms due to the
flexibility of atomic arrangements, inducing uncertainties in representation.
To this end, we propose PolySRL, which learns the probabilistic representation
of stoichiometry by utilizing the readily available structural information,
whose uncertainty reveals the polymorphic structures of stoichiometry.
Extensive experiments on sixteen datasets demonstrate the superiority of
PolySRL, and analysis of uncertainties shed light on the applicability of
PolySRL in real-world material discovery. The source code for PolySRL is
available at https://github.com/Namkyeong/PolySRL_AI4Science.