Revolutionary AI method unlocks the secrets of friction!

Revolutionary AI method unlocks the secrets of friction!

Friction is a ubiquitous phenomenon that occurs in many technical and biological systems, from engines to technical devices to human joints. Despite their everyday presence, the physical processes involved in friction are often complex and difficult to investigate experimentally. However, a new simulation method developed at the University of Freiburg and the Karlsruhe Institute of Technology (KIT) could change this. This method uses artificial intelligence (AI) to analyze friction at the molecular level and make better predictions.

The results of the researchers in the journalScience Advancespublished show that the combination of physical models at different length scales and machine learning methods provides promising insights. According to Professor Peter Gumbsch from KIT, this technique can provide a deeper understanding of complex friction systems, which is of great interest to industry and materials science. Professor Lars Pastewka from the University of Freiburg adds that this innovative method enables realistic predictions of friction, which can be crucial for the development of new, low-friction systems and long-lasting materials.

The new simulation method

The newly developed method describes friction more precisely and transfers the processes to larger, technically relevant systems. It increases the accuracy and efficiency when simulating tribological systems. These approaches are particularly valuable because understanding friction is often based on inaccurate assumptions. Computer simulations help researchers better understand the complex mechanisms of friction, lubrication and the associated wear.

A central element of this method is the use of active learning methods, which make it possible to continuously improve the underlying models by generating new training data. Dr. Hannes Holey, lead author of the study, describes the method as a breakthrough in understanding complex friction systems. It is not just an academic advance, but a promising basis for the development of materials and systems that could be more efficient and perform better in the future.

The interdisciplinary approach

The interdisciplinary collaboration between the institutes illustrates how artificial intelligence is becoming increasingly important in materials science. AI is used not only to predict material properties but also to discover new materials from unexplored chemical structural spaces. These technologies revolutionize access to new materials and significantly expand the possibilities in the field of materials research. Database initiatives such as PoLyInfo are facilitating access to needed information resources, although challenges in data sharing and standardization remain.

Artificial intelligence in materials science gives rise to a variety of approaches, such as Bayesian optimization and random forest regressors, that are used to predict the properties of materials. An example of progress in this area is the first successful prediction of a new material from chemical white space, achieved using a random forest regressor.

Overall, the collaboration between research institutions and the use of AI in friction research shows how powerful new technologies are. The connection between physical models and intelligent algorithms could be crucial for the development of new, long-lasting materials and systems in the near future.

KIT reports about this news and the important role of AI in modern materials science, while the University of Freiburg in its Press release underlines the transformative power of this innovation. Further information about artificial intelligence in materials science can be found in the Wikipedia.