A recent systematic experimental characterisation of technological thin films, based on elaborated design of experiments as well as probe calibration and correction procedures, allowed for the first time the determination of nanoscale friction under the concurrent influence of several process parameters, comprising normal forces, sliding velocities, and temperature, thus providing an indication of the intricate correlations induced by their interactions and mutual effects. This created the preconditions to undertake in this work an effort to model friction in the nanometric domain with the goal of overcoming the limitations of currently available models in ascertaining the effects of the physicochemical processes and phenomena involved in nanoscale contacts. Due to the stochastic nature of nanoscale friction and the relatively sparse available experimental data, meta-modelling tools fail, however, at predicting the factual behaviour. Based on the acquired experimental data, data mining, incorporating various state-of-the-art machine learning (ML) numerical regression algorithms, is therefore used. The results of the numerical analyses are assessed on an unseen test dataset via a comparative statistical validation. It is therefore shown that the black box ML methods provide effective predictions of the studied correlations with rather good accuracy levels, but the intrinsic nature of such algorithms prevents their usage in most practical applications. Genetic programming-based artificial intelligence (AI) methods are consequently finally used. Despite the marked complexity of the analysed phenomena and the inherent dispersion of the measurements, the developed AI-based symbolic regression models allow attaining an excellent predictive performance with the respective prediction accuracy, depending on the sample type, between 72% and 91%, allowing also to attain an extremely simple functional description of the multidimensional dependence of nanoscale friction on the studied variable process parameters. An effective tool for nanoscale friction prediction, adaptive control purposes, and further scientific and technological nanotribological analyses is thus obtained.

A structured transdisciplinary method for the experimental determination of friction in the nanometric domain is proposed in this paper. The dependence of nanoscale friction on multiple process parameters on these scales, which comprise normal forces, sliding velocities, and temperature, was studied via the lateral force microscopy approach. The procedure used to characterize the stiffness of the probes used, and especially the influence of adhesion on the obtained results, is thoroughly described. The analyzed thin films were obtained by using either atomic layer or pulsed laser deposition. The developed methodology, based on elaborated design of experiments algorithms, was successfully implemented to concurrently characterize the dependence of nanoscale friction in the multidimensional space defined by the considered process parameters. This enables the establishment of a novel methodology that extends the current state-of-the-art of nanotribological studies, as it allows not only the gathering of experimental data, but also the ability to do so systematically and concurrently for several influencing variables at once. This, in turn, creates the basis for determining generalizing correlations of the value of nanoscale friction in any multidimensional experimental space. These developments create the preconditions to eventually extend the available macro- and mesoscale friction models to a true multiscale model that will considerably improve the design, modelling and production of MEMS devices, as well as all precision positioning systems aimed at micro- and nanometric accuracy and precision.