Machine learning force fields (MLFFs) are powerful tools for materials
modeling, but their performance is often limited by training dataset quality,
particularly the lack of rare event configurations. This limitation undermines
their accuracy and robustness in long-time and large-scale molecular dynamics
simulations. In this work, we present a hybrid MLFF framework that integrates
an empirical short-range repulsive potential and demonstrates improved
robustness and training efficiency. Using solid electrolyte
Li$_7$La$_3$Zr$_2$O$_{12}$ (LLZO) as a model system, we show that purely
data-driven MLFFs fail to prevent unphysical atomistic clustering in extended
simulations due to inadequate short-range repulsion. In contrast, the hybrid
force field eliminates these artifacts, enabling stable long-time simulations,
which are critical for studying various properties of LLZO. The hybrid
framework also reduces the need for extensive active learning and performs well
with just 25 training configurations. By combining physics-driven constraints
with data-driven flexibility, this approach is compatible with most existing
MLFF architectures and establishes a universal paradigm for developing robust,
training-efficient force fields for complex material systems.

Cet article explore les excursions dans le temps et leurs implications.

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