BIGDML: Efficient Gradient-Domain Machine Learning Force Fields for Materials

The construction of accurate and efficient machine learning (ML) force fields for materials remains an unsolved challenge. Here we introduce Bravais-Inspired GDML[1,2] (BIGDML) model, with which we are able to construct meV-accurate force fields for materials using a training set with just 10-100 geometries. The global BIGDML model does not assume localization of atomic interactions and enables the direct reconstruction of force fields for a wide variety of extended systems (e.g. bulk materials, interfaces, molecular crystals, defects) with high data efficiency and state-of-the-art force accuracies (< 1 kcal/mol/Å). We present a challenging application of BIGDML to the dynamics of benzene adsorbed on graphene, which requires only 30 training geometries to achieve such an accuracy. The BIGDML framework extends the applicability of machine learning to increasingly complex periodic materials.

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