Explanatory challenges in chemistry in the case of machine learning, the effect of prevalent reductionistic thinking and arising epistemic risks

Project description

“Scientific explanation” and “scientific understanding” are central concepts and aims of scientific work. And even beyond science, trust in scientific statements is often measured by looking at the explanations and understanding they provide. To better comprehend this, and to avoid that new scientific approaches are too quickly dismissed as unscientific, the project will first analyze these terms in the classical sciences. In this work, I refer to the example of (physical) chemistry, in particular its attempts to explain and/or understand chemical bonding with the help of quantum mechanics. This project is located in a subordinate part of philosophy, the philosophy of science, and, more specifically, in the philosophy of chemistry. It uses methods and tools of (analytical) philosophy.

The concepts of the central terms explanation and understanding are subject to changes and challenges as a result of new developments in science. Newly emerging methods of machine learning, such as neural networks, open up a multitude of new possibilities for research. It is therefore necessary to investigate whether classical explanations are sometimes pushed into the background. The application of the new methods also influences the genesis of scientific theories, the synthesis of new compounds, or the prediction of substance properties. There seem to be the need to investigate if classical explanations are driven back by these new methods. The dissertation project is assigned to the interdisciplinary DFG research training group “Tailored Scale-Bridging Approaches to Computational Nanoscience” (RTG 2450) and uses on the one hand its research in the field of materials science as a starting point for philosophical investigations. On the other hand, an interdisciplinary exchange is intended to take place in direct contact with expert scientists. Another field of research is didactic literature, such as basic literature in the form of textbooks. With these, underlying assumptions in the argumentation of chemical theories should be revealed and critically discussed. In this way, the influence of education on theoretical work in chemistry and, conversely, the influence of current scientific knowledge and debates on education can be examined.

In the projects of the research training group and in chemistry in general, different scales (time, size, ...) are of great importance. A material to be synthesized can be examined at the macroscopic, microscopic, or nano level using different methods and the underlying theories. In the same way, scientific theories are related to each other, which is described by the so-called (theory) reduction. The influence of reductionist relationships is also investigated in this project. In addition, associated epistemic risks are considered and classified.

Administrative data

Supervisor: Prof. Dr. Dr. Rafaela Hillerbrand
Advisor: Prof. Dr. Marcus Elstner
Related projects: DFG Research Training Group 2450: Tailored Scale-Bridging Approaches to Computational Nanoscience
Doctoral students at ITAS: see Doctoral studies at ITAS


Oliver Bott, M.Ed.
Karlsruhe Institute of Technology (KIT)
Institute for Technology Assessment and Systems Analysis (ITAS)
P.O. Box 3640
76021 Karlsruhe

Tel.: +49 721 608-26164