The space of possible materials is unimaginably large. To find our way in this space, having a map that can guide us would be nice. In this presentation, we show that machine learning can provide us with such a map. We can use machine learning to encode patterns that are tacit or hidden in a large number of dimensions of this chemical space, and then use it to guide the design of materials. The simplest application of this navigation system is to predict properties that are hard to predict with conventional quantum chemistry or molecular simulation alone.
Once we have this in place, we can use it to most efficiently gather information about structure-property-function relationships. A fundamental difficulty here is, however, that we often have to deal with multiple, often competing objectives. For instance, increasing the reactivity often decreases the selectivity. Interestingly, one can show that using a geometric construction; one can also effectively, and without bias, use machine learning to dramatically accelerate materials design and discovery in such a multiobjective design space. It is important to realize, however, that machine learning relies on data that a machine can use. Toward this goal, we need to develop infrastructure to allow for the capture without overhead while providing chemists with tools that simplify their daily work. A challenge, however, is that data typically cannot be easily collected in this nice tabular firm. Recent advantages of applying large language models (LLMs) to chemistry indicate that they might be used to address this challenge. I will showcase how LLMs can autonomously use tools, leverage structured data as well as soft inductive biases, and, in this way, transform how we model chemistry.