In this context, the term gene is used for functional groups of elements acting as descriptors for a particular property. Genes are presented as fictive chemical entities with a characteristic stoichiometry derived from SLCA. They do not need to be charge-balanced, and may not even require that their constituents are chemically connected, thus, they do not require a structural representation in real space (although such knowledge could be helpful in their further interpretation). As an example, three functional groups which could be responsible for ion conductivity are depicted schematically in Figure . All of these groups involve the gene ABC2 as a common feature, whereas further constituents (depicted as green balls) could be any of the three elements A,B and C, or even additional, unknown components. In this schematic example, the gene of ABC2 would have been extracted by SLCA as a fundamental descriptor for ionic conductivity in the specific type of glass. Technically, the present method is based on an optimization problem that maximizes the R 2 from linear regression between a property (here: ionic conductivity) and the sum of weighted concentrations of each constituent by tuning the weighting factors. This optimization is done for all subsets of composition -property data (that is, for all possible combinations of elements; each combination is handled as an individual model). Assisted by matrix rotation, principal component analysis (PCA, ) and family trees, those models which are essential for the studied property are identified, and from their characteristic mean composition, the essential genes. Thereby, the absolute concentration of a gene is initially undetermined; other than with common regression analysis or similar machine learning approaches which are frequently applied in glass science , relationships between the weighted sum of certain elemental components and a target property are not in the focus of the present study. The genes, however, may act as descriptors in physics-informed ML models for glass property predictions.