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KFM: Fachverband Kristalline Festkörper und deren Mikrostruktur
KFM 14: Mechanical Properties and Alloy Design: e.g. Light-Weight, High-Temperature, Multicomponent Materials (joint session MM/KFM)
KFM 14.2: Vortrag
Mittwoch, 20. März 2024, 12:00–12:15, C 230
Training strategies for machine-learning potentials suitable to simulate mechanical response of ceramics — •Shuyao Lin1,2, Zhuo Chen3, Luis Casillas-Trujillo2, Ferenc Tasnadi2, Zaoli Zhang3, Lars Hultman2, Paul H. Mayrhofer1, Davide G. Sangiovanni2, and Nikola Koutna1,2 — 1Institute of Materials Science and Technology, TU Wien, A-1060, Vienna, Austria — 2Department of Physics, Chemistry, and Biology (IFM), Linköping University, SE-58183, Linköping, Sweden — 3Erich Schmid Institute of Materials Science, Austrian Academy of Sciences, A-8700, Leoben, Austria
Machine-learning interatomic potentials (MLIPs) offer a powerful avenue for simulations beyond length and timescales of ab initio methods. In particular, MLIPs enable investigations of mechanical properties and fracture behaviour of materials with supercell sizes, loading geometries and temperatures relevant for real operation conditions. Using the example of hard TiB2 ceramic, in this talk we propose a strategy for fitting MLIPs suitable to simulate mechanical response of monocrystals from atomic to nanoscale, including strains until fracture and deformation-induced phase transformations. After validation, the best-performing MLIP is employed to carry out molecular dynamics simulations of various loading conditions, with main focus on tensile and shear deformation. Consequently, we derive size-dependent trends in theoretical strength, toughness, and crack initiation patterns of TiB2. To approach experimental observations, we additionally apply our MLIP to models containing a pre-crack and/or grain boundaries.
Keywords: Machine Learning Interatomic Potentials; Diborides; Ab initio Molecular Dynamics; Mechanical Properties; Size effect