Metal-insulator   transition   (MIT)   compounds   are   materials   that   can undergo an electronic phase changes and are promising platforms to build next-generation   low-power   microelectronics.   Accelerated   discovery   is challenging    using    high-throughput    screening    because    high-fidelity quantum-mechanical    simulations    are    computationally    prohibitive    to perform. We solved this problem by building a supervised machine-learning model  that  can  classify  whether  a  material,  given  its  structure  as  input, would exhibit a thermal MIT.

• •We   created   the   first   the   public   dataset   of   thermally-driven   MIT compounds     using     natural     language     processing     schemes     in collaboration  with  the  Olivetti  group  (at  MIT)  and  publicly  disseminated the new database.
• •Compounds  were  featurized  using  new  physics-based  descriptors, which  were  integrated  into  the matminer  Python  library  for data  mining the properties of materials.
• •We also deployed an easy-to-use online pipeline that can enable quick probabilistic of any crystalline material. Promising compounds identified with    classification  model  are  undergoing  experimental  validation,  in collaboration with the Wilson group at UCSB.