The prediction of novel phases with advanced properties (chemical, thermal, mechanical, electronic, etc.) is a crucial research topic, for which theoretical approaches have recently been developed including computational materials discovery using evolutionary crystal structure prediction and deep machine learning . Despite the availability of structure discovery tools such as Universal Structure Predictor: Evolutionary Xtallography (USPEX) and machine learning programs such as Graph Networks for Materials Exploration (GNoME) , the rationalizing role of the scientist (crystallographer, physicist, and chemist) remains unavoidable to establish the relationship between structure, crystal chemistry, and properties to find the optimal way to synthesize new materials.