Recently, novel methods based on materials informatics and machine-learning models have emerged to assist the search for materials with improved properties in industrially relevant applications. To apply this approach in heteroanionic materials discovery, NU-MRSEC IRG-2 has reported a computational investigation of a series of ternary HetMs with tunable band gaps from machine-learning and crystal structure prediction.