simulation group

2025

1. Active learning for nonparametric multiscale modeling of boundary lubrication

   Hannes Holey, Peter Gumbsch, and Lars Pastewka

   Sci. Adv. 11, eadx4546 (2025)

   Abstractdoi

   Lubricant films are essential in everyday life. Current rheological models, embodied in the empirical Stribeck curve, describe the frictional response of lubricated contacts in distinct regimes: boundary, mixed, and hydrodynamic lubrication. This fragmented description is problematic because lubricated contacts often transition between these regimes. We present a nonparametric method to learn constitutive laws in the boundary lubrication regime directly from molecular simulations. Machine-learning-based constitutive laws can be combined with continuum simulations to span the full range of Stribeck behavior. We use this framework to demonstrate that the complexity of boundary lubrication is captured by a single machine-learned material function.

2. Modeling in tribology: Recent advances, applications, and open questions

   Lars Pastewka, Antonis I. Vakis, Ramin Aghababaei, Andreas Almqvist, Giuseppe Carbone, Michael Chandross, Daniele Dini, Stefan J. Eder, Hendrik J. Ehrich, James P. Ewen, Nicola Menga, Jean-François Molinari, Gianpietro Moras, Lucia Nicola, Marco Paggi, Carmine Putignano, Michele Scaraggi, Vladislav A. Yastrebov, and Martin H. Müser

   Tribol. Int., 111326 (2025)

   Abstractdoi

   This review provides a comprehensive overview of recent advances in computational modeling of tribological phenomena, covering scales from electronic structure to continuum mechanics. We discuss applications to friction, wear, lubrication, and contact mechanics, and identify key open questions for future research.

3. The surface-topography challenge: A multi-laboratory benchmark study to advance the characterization of topography

   A. Pradhan, M. H. Müser, N. Miller, J. P. Abdelnabe, L. Afferrante, D. Albertini, D. A. Aldave, L. Algieri, N. Ali, A. Almqvist, T. Amann, P. Ares, B. N. Balzer, L. Baugh, E. A. Berberich, M. Björling, M. S. Bobji, F. Bottiglione, B. Brodmann, W. Cai, G. Carbone, R. W. Carpick, F. Cassin, J. Cayer-Barrioz, M. I. Chowdhury, M. Ciavarella, E. Cihan, D. Huang, E. Delplanque, A. J. Deptula, S. Descartes, A. Dhinojwala, M. Dienwiebel, D. Dini, A. C. Dunn, C. Edwards, M. Eriten, A. Esawi, R. M. Espinosa-Marzal, L. Fang, A. Fatemi, C. Fidd, D. Gabriel, F. Gaslain, G. Giordano, J. Gómez-Herrero, L. Gontard, N. N. Gosvami, G. Greenwood, C. Greiner, T. Grejtak, A. Haroun, M. Hasan, S. Hoppe, L. Isa, R. L. Jackson, S. Jang, O. Johnson, F. Kaiser, M. Kalin, K. Kalliorinne, P. H. Karanjkar, S. H. Kim, S. Kinzelberger, P. Klapetek, B. A. Krick, C. Kumar, N. Kumar, S. Kumar, P. LaMascus, R. Larsson, P. Laux, M. J. Lee, P. M. Lee, W. Lee, C. Leriche, J. Li, Y. Li, Y.-S. Li, T. A. Lubrecht, I. A. Lyashenko, C. Ma, T. Ma, F. Maaboudallah, S. Mahmood, F. Mangolini, M. Marian, D. Mazuyer, Y. Meng, N. Menga, T. Miller, D. M. Mulvihill, M. Najah, D. Nečas, C. I. Papadopoulos, A. Papangelo, M. Pauli, B. N. J. Persson, A. Peterson, A. A. Pitenis, P. Podsiadlo, M. Polajnar, V. L. Popov, T. Požar, A. Prasad, G. Prieto, C. Putignano, M. H. Rahman, S. B. Ramisetti, S. Raumel, I. J. Reyes, N. Rodriguez, M. Rodríguez Ripoll, H. Rojacz, P. Sainsot, A. Samodurova, D. Savio, M. Scaraggi, F. Schaefer, S. W. Scherrer, K. D. Schulze, K. E. Shaffer, M. A. Sidebottom, D. Skaltsas, J. Soni, C. Spies, G. W. Stachowiak, L. Steinhoff, N. C. Strandwitz, K. Sun, S. Tripathi, W. R. Tuckart, S. Ugar, M. Valtr, K. E. Van Meter, J. Vdovak, J. G. Vilhena, G. Violano, G. Vorlaufer, M. Walczak, B. Weber, T. Woloszynski, M. Wolski, A. Yadav, V. A. Yastrebov, M. Yongjian, L. Yuan, J. Yus, J. Zhang, X. Zhang, Q. Zheng, L. Pastewka, and T. D. B. Jacobs

   Tribol. Lett. 73, 110 (2025)

   Abstractdoi

   Surface topography measurements are critical for understanding and predicting material properties and behaviors. However, reproducibility of these measurements across different laboratories and instruments remains a significant challenge. This multi-laboratory study benchmarked surface characterization across more than 60 laboratories worldwide to assess reproducibility and identify sources of variability.

4. Transient formation of single layer diamond during friction force microscopy of SiC-supported epitaxial graphene

   Milad Zarshenas, Takuya Kuwahara, Bartosz Szczefanowicz, Andreas Klemenz, Leonhard Mayrhofer, Thomas Reichenbach, Gernot Pennsiller, Dominik Ziegler, Jens Gobrecht, Christoph Zysset, Gianpietro Moras, Derya Öner, Fevzi Öner, Udo D Schwarz, and Michael Moseler

   Adv. Mater. Interfaces 12, e00511 (2025)

   Abstractdoi

   The transient formation of single-layer diamond during friction force microscopy of SiC-supported epitaxial graphene is revealed through combined experimental and computational investigations.

2024

1. Small-scale roughness entraps water and controls underwater adhesion

   Nityanshu Kumar, Siddhesh Dalvi, Anirudha V Sumant, Lars Pastewka, Tevis D B Jacobs, and Ali Dhinojwala

   Sci. Adv. 10, eadn8343 (2024)

   Abstractdoi

   While controlling underwater adhesion is critical for designing biological adhesives and in improving the traction of tires, haptics, or adhesives for health monitoring devices, it is hindered by a lack of fundamental understanding of how the presence of trapped water impedes interfacial bonding. Here, by using well-characterized polycrystal diamond surfaces and soft, nonhysteretic, low-surface energy elastomers, we show a reduction in adhesion during approach and four times higher adhesion during retraction as compared to the thermodynamic work of adhesion. Our findings reveal how the loading phase of contact is governed by the entrapment of water by ultrasmall (10-nanometer-scale) surface features. In contrast, the same nanofeatures that reduce adhesion during approach serve to increase adhesion during separation. The explanation for this counterintuitive result lies in the incompressibility-inextensibility of trapped water and the work needed to deform the polymer around water pockets. Unlike the well-known viscoelastic contribution to adhesion, this science unlocks strategies for tailoring surface topography to enhance underwater adhesion.