Science is and always has been based on data, but the terms 'data-centric' and the '4th paradigm' of materials research signifies a transformative shift towards retrieving and managing vast data collections, digital repositories, and innovative data analytics methods. The integration of AI and its subset ML has become pivotal in addressing all these challenges. In the data analysis, we are looking for structures and patterns in the data. As mentioned above, materials properties and function are often not just governed by one single process but there are many. Some drive, others just facilitate, and again others hinder the materials property or function of interest. The interplay of various processes is very intricate. In analogy to genes in biology, we discuss elemental materials features (e.g., electronegativity of the atoms that build the material) that correlate with the materials property of interest. The primary features that connect with of a certain materials property or function are called the relevant 'materials genes'. Together with environmental parameters (e.g., temperature), they determine (in a statistical sense) the material's property and function. In recent years, major advances in ML and computing power, in particular the advance of hardware accelerators like GPUs, have enabled deep neural networks, with billions of trainable parameters, leading to breakthroughs in computer vision and natural language processing. A key strength of deep learning is that it addresses not only the objective for classification, regression or other tasks, but also the learning of how to represent the input data itself. Thus, there is no need for explicit feature modeling: images can be ingested as arrays of pixels, and text documents are simply sequences of tokens. High-level structures in visual or textual contents, like people interacting with objects in a scene or argumentation and sentiments in a conversation, are automatically discovered and latently captured by the deep neural network itself.