As technologies progress, the development of new mechanical systems demands the rapid determination of friction coefficients of materials. Data mining and materials informatics methods are used here to generate a predictive model that enables efficient high-throughput screening of ceramic materials, some of which are candidate high-temperature, solid-state lubricants. Through the combination of principal component analysis and recursive partitioning using a small dataset comprised of intrinsic material properties, we develop a decision tree-based model comprised of if-then rules which estimates the friction coefficients of a wide range of materials. This datadriven model has a high degree of accuracy with an R 2 value of 0.8904 and provides a range of possible friction coefficients that accounts for the possible variability of a material's actual friction coefficient.
All Science Journal Classification (ASJC) codes
- Mechanics of Materials
- Mechanical Engineering
- Surfaces and Interfaces
- Surfaces, Coatings and Films