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| Hauptseite > Publikationsdatenbank > Adaptive-precision interatomic potentials (APIP) |
| Hauptseite > Publikationsdatenbank > Adaptive-precision interatomic potentials (APIP) |
| Poster (After Call) | FZJ-2026-04291 |
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2026
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Please use a persistent id in citations: doi:10.34734/FZJ-2026-04291
Abstract: Computer simulations of atomistic systems are a cornerstone of research in Physics, Chemistry, Biology and Materials Science and account for a large share of applications on the worlds supercomputers and high-performance computing clusters. Large-scale atomistic simulations rely on interatomic potentials providing an efficient representation of atomic energies and forces. Although modern machine learning (ML) potentials provide a density-functional theory-like accuracy, they are rarely used in large-scale simulations as they remain orders of magnitude slower than simple empirical potentials. Often only a small number of atoms strongly influences the correct outcome of a simulation, while the majority of atoms forms a background for proper field propagation or mechanical properties on a coarser level. Examples include bond-breaking at a crack tip for simulations of crack propagation, defect nucleation sites in simulations of plastic deformation, or reaction centres in biology. An adaptive-precision interatomic potential (APIP) overcomes the performance gap between the precise ML and the fast empirical potentials for such simulations: An APIP combines two potentials of different accuracy and computational costs to a multi-resolution description with an optimum of performance and precision in large complex atomistic systems. The required precision is determined per atom by a local structure analysis and updated automatically during simulation. Our APIP package of the molecular-dynamics simulator LAMMPS makes APIPs available to the community. A load-balancer prevents problems due to the atom dependent force-calculation times, which makes it suitable for large-scale atomistic simulations. Copper and tungsten have been used as demonstrator materials for nanoindentation simulations with embedded atom models as fast potentials and atomic cluster expansions as precise ML potentials, but in principle a broader class of potential combinations could be implemented. All ML-specific observations were reproduced by the APIPs, however, with a significant speedup of 20-30 times compared to the pure ML-potential nanoindentation simulations. The APIP-package supports conservative potentials, i.e., the system can be described by a momentum-conserving Hamiltonian. Alternatively, one can use performance-optimised APIPs that aim for the highest speedup possible and conserve energy and momentum due to local corrections. APIPs can achieve – dependent on both the combined potentials and the atomistic system – a speedup of one or two orders of magnitude compared to a pure precise simulation. Thus, the coupling of fast and precise models is very promising and allows for simulations of either much larger system sizes or diminished resource requirements at constant simulation size.
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