Screening and mechanism analysis of negative thermal expansion in Bi0.85Ca0.15Fe0.85M0.15O3 (M: tetravalent cations) via machine learning force fields

Negative thermal expansion (NTE) is a key property for achieving dimensional stability in advanced technologies,13 yet the discovery of new NTE materials is often hindered by the vast compositional space and the complexity of 14 phase transition mechanisms.In this study, we applied large-scale molecular dynamics simulations using the 15 Crystal Hamiltonian Graph Network (CHGNet), a pretrained universal machine learning force field (MLFF), to 16 screen for NTE candidates in the Bi 0.85 Ca 0.15 Fe 0.85 M 0.15 O 3 system (M = Ti, Zr, Sn, Hf, Ir, Pb, Ru).Our systematic 17 simulations reveal that the co-substitution of Ca and tetravalent cations effectively modulates the phase stability of 18 BiFeO 3 , with the transition temperature varying depending on the substituting element.Notably, the Ir-substituted 19 compound exhibits a sharp volume-collapsing first-order phase transition at lower temperature (~380 K) with a 20 substitution level of 15% in good agreement with the experimental result.These findings demonstrate that 21 universal MLFFs are powerful tools for accelerating the exploration of functional materials and clarifying atomic-22 level mechanisms of complex phase transitions.23