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The potential for bridgmanite megacrysts to drive magma ocean segregation

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Abstract

Earth’s early mantle probably existed as a deep, vigorously convecting magma ocean, and its solidification is considered central to the long-term chemical and dynamical evolution of the planet. Yet a notable uncertainty is the grain size of bridgmanite—the dominant lower-mantle phase—whose nucleation behaviour at extreme pressure has remained experimentally inaccessible. Here we show, using a combination of cutting-edge techniques, including large-scale molecular dynamics simulations consisting of up to 1 million atoms driven by machine learning potentials (MLPs), seeding and enhanced sampling, that crystal–melt interfacial energies of MgSiO3 bridgmanite increase substantially with pressure, surpassing those of silicate–liquid systems at ambient pressure by a factor of up to ten (refs. 1,2,3). In a deep basal magma ocean (BMO), this amplified interfacial energy, combined with the potential sluggish cooling, may permit the formation of unusually large bridgmanite crystals, up to centimetre-to-metre-scale sizes. Such potentially large crystals could drive efficient fractional crystallization and cause substantial chemical differentiation and mantle compaction. If operative, this mechanism would provide a new physical pathway linking lower-mantle material properties to early Earth stratification and it motivates future geodynamic models that explicitly incorporate supercooling, compositional convection and elemental partitioning. Our findings thus offer a plausible hypothesis connecting microscopic nucleation processes with macroscopic planetary structure, refining present views of how the Earth’s interior acquired its initial compositional architecture.

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Fig. 1: Simulation of the crystallization and melting processes of MgSiO3 bridgmanite.
Fig. 2: Interfacial energies of silicate-melt as a function of pressure and temperature.
Fig. 3: Estimated grain size of MgSiO3 bridgmanite.
Fig. 4: Schematic illustrating one possible evolution of grain-size distribution during magma ocean cooling under a bottom-up solidification scenario.

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Data availability

The main data supporting the findings of this study are available in the paper and its Supplementary Information. The raw data used to train the machine learning potential of olivine are stored in the Open Science Framework at https://osf.io/kf9wb/ with https://doi.org/10.17605/OSF.IO/KF9WB.

Code availability

The software packages used in this study are standard: VASP (version 5.4) (a commercial code package; see www.vasp.at), DeePMD-kit (https://github.com/deepmodeling/deepmd-kit), phonopy (http://phonopy.github.io/phonopy/), LAMMPS (https://www.lammps.org/), PLUMED 2 (https://www.plumed.org/doc-v2.6/user-doc/html/index.html).

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Acknowledgements

We thank V. Solomatov, Z. Du, J. Wang, M. Chen and H. Luo for discussions and Y. Peng for the assistance with simulations. We acknowledge the following grants: National Science Foundation (EAR-2223935 to L.S.), National Natural Science Foundation of China (grant no. 92370118 to H.N.), the Research Fund of the State Key Laboratory of Solidification Processing (NPU), China (grant no. 2024-ZD-01 to H.N.) and the Fundamental Research Funds for the Central Universities. The simulations presented in this article are performed on computational resources managed and supported by Princeton Research Computing, a consortium of groups including the Princeton Institute for Computational Science and Engineering (PICSciE) and the Office of Information Technology’s High Performance Computing Center and Visualization Laboratory at Princeton University. Simulations are also performed at the local cluster in H.N.’s group.

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  1. These authors contributed equally: Jie Deng, Junwei Hu

Authors and Affiliations

  1. Department of Geosciences, Princeton University, Princeton, NJ, USA

    Jie Deng & Jina Lee

  2. State Key Laboratory of Solidification Processing, International Center for Materials Discovery, School of Materials Science and Engineering, Northwestern Polytechnical University, Xi’an, People’s Republic of China

    Junwei Hu, Yidi Shi & Haiyang Niu

  3. Department of Earth, Planetary, and Space Sciences, University of California, Los Angeles, Los Angeles, CA, USA

    Lars Stixrude

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Contributions

J.D. and L.S. conceived the original idea. J.D. and H.N. conceived and coordinated the entire project. J.H., Y.S., H.N. and J.D. performed theoretical calculations and modelling. J.L. provided feedback on data interpretation and analysis. J.D. wrote the first draft. All authors contributed to the discussion and revision of the manuscript.

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Correspondence to Jie Deng or Haiyang Niu.

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Extended data figures and tables

Extended Data Fig. 1 Structure factors and collective variables.

a, The simulated structure factors of bridgmanite (blue) and liquid (orange) using only Mg and Si atoms at 25, 50, 75, 100, 125 and 140 GPa. b, Probability distributions of the collective variables Si for the bridgmanite and liquid phases at 25, 50, 75, 100, 125 and 140 GPa. The dashed line denotes D(P), the maximum value of the Gaussian distribution of Si in the liquid phase under the respective pressures.

Source Data

Extended Data Fig. 2 Critical cluster size of nucleus NC estimated from molecular dynamics simulations.

All molecular dynamics simulations start with an identical initial configuration. The solid curves depict the fluctuation of nucleus sizes over time at 5,075 K (red), 5,100 K (blue) and 5,125 K (grey) at 125 GPa. The horizontal dashed line represents the estimated NC value, with the error bar indicated by the light blue bar. 5,075 K and 5,125 K are below and above the critical temperature that corresponds to this initial nucleus and thus the nucleus grows and dissolves, respectively. 5,100 K, on the other hand, is very close to the critical temperature and therefore has a nearly equal chance of dissolution and growth. As such, one simulation ends with nucleus growth and the other ends with dissolution.

Source Data

Extended Data Fig. 3 A snapshot with the nucleus crystallized from the melt.

Simulation is at 100 GPa with a system comprising 1,023,120 atoms. Only Mg and Si atoms are shown, represented by red and blue colours, respectively. Liquid-like atoms are shown with a transparent colour. In the right panel, the regions enclosed by the green surface serve to highlight the spherical-like nucleus that crystallized from the melt.

Extended Data Fig. 4 Growth and melting process of MgSiO3 bridgmanite.

a–d, Simulations are at 100 GPa with varying system sizes. The simulation cells comprise of 16,000 atoms with a box length of about 5 nm (a), 140,800 atoms with a box length of about 10 nm (b), 491,520 atoms with a box length of about 15 nm (c) and 1,023,120 atoms with a box length of about 20 nm (d), respectively. The middle panel illustrates the initial configurations of the four systems, with a crystal nucleus corresponding to a size of approximately 3.5 nm serving as a seed. The left and right panels exhibit the melting and growth of the crystal at the respective marked temperatures. Only Mg atoms and Si atoms are shown in the snapshots and they are coloured with the value of D(P), in the same way as shown in Fig. 1b.

Extended Data Fig. 5 System size convergence of interfacial energy.

The interfacial free energy plotted against system size (that is, the number of atoms in the simulation cell). The simulation cells consist of 7,680, 11,340, 16,000, 140,800, 491,520 and 1,023,120 atoms, respectively.

Source Data

Extended Data Fig. 6 Thermophysical properties and diffusivity of the MgSiO3 system.

a, Chemical potential difference between the liquid and the crystalline MgSiO3 (Δμ) at 125 GPa calculated by the empirical formula (orange) \(\Delta \mu =\Delta {H}_{{\rm{m}}}\left(1-\frac{T}{{T}_{{\rm{m}}}}\right)\) and thermodynamic integration (blue), respectively. b, Pressure–volume results of MgSiO3 bridgmanite at 1,000 K (cyan), 2,000 K (blue), 3,000 K (green), 4,000 K (red), 5,000 K (magenta) and 6,000 K (yellow). The fitted curves are calculated with the best-fitting equation of state parameters. The calculated pressure–temperature–volume (P–V–T) results are curve-fit to the following equation P(V, T) = P(V, T0) + BTH(T − T0), in which P(V, T0) represents the reference isotherm corresponding to T0 = 2,000 K, using a fourth-order Birch–Murnaghan equation of state and a volume-dependent \({B}_{{\rm{TH}}}(V)=\left[a-b\left(\frac{V}{{V}_{0}}\right)+c{\left(\frac{V}{{V}_{0}}\right)}^{2}\right]/1,\,000\), in which a, b and c are constants, V0 = 24.24 cm3 mol−1 and the corresponding reference pressure P0 at 2,000 K is 20 GPa. The best-fitting parameters are K0 = 258.00 ± 1.53 GPa, K′ = 4.22 ± 0.11, K″ = −0.021 ± 0.003 GPa−1, a = 16.22 ± 3.18, b = 20.75 ± 7.24 and c = 1.66 ± 4.18. c, Enthalpy of melting of MgSiO3 bridgmanite (black dots) along with the best-fitting curve, that is, \(\Delta {H}_{{\rm{m}}}\,({\rm{k}}{\rm{J}}\,{{\rm{m}}{\rm{o}}{\rm{l}}}^{-1})=-0.0115{P}^{2}+3.287P+108.118\), in which P is pressure in GPa. d, Diffusivity of Si along the peridotitic (dashed)22 and chondritic (solid)21,46 liquidi, respectively.

Source Data

Extended Data Fig. 7 Effects of iron on interfacial energy.

Interfacial energies of Fe-bearing bridgmanite liquid (thick lines) and those of Fe-free counterparts (thin lines) along the peridotitic (dashed lines)22 and chondritic (solid lines)21 liquidi, respectively.

Source Data

Extended Data Fig. 8 Heterogeneous nucleation with the embryo of bridgmanite nucleating on periclase in parent-phase silicate liquid.

a, Schematic of heterogeneous nucleation configuration. b, Contact angle as a function of pressure along the peridotitic (dashed line)22 and chondritic (solid line)21 liquidi. c, The ratio of apparent interfacial energy to homogeneous nucleation interfacial energy for various contact angles and x is the ratio of the periclase crystal size R and critical nucleation size (r*), x = R/r*. The red-shaded region indicates plausible values of contact angles from b.

Source Data

Extended Data Fig. 9 Diffusivity of Si in MgSiO3 liquid.

Dashed and solid lines represent the values along the peridotitic22 and chondritic21,46 liquidi, respectively.

Source Data

Extended Data Fig. 10 Nucleation rate of bridgmanite at 115 (blue), 125 (green) and 140 GPa (red) as a function of the temperature.

The symbols are the calculated results and the solid lines are the corresponding best fits. The dashed lines are adiabats of a cooling magma ocean of a chondritic bulk composition take from ref. 27. Overall, the nucleation rate at 140 GPa is around eight orders of magnitude higher than that at 115 GPa any time along the adiabat. Predicted grain sizes are sensitive to the assumed cooling rate. Values shown are illustrative for the chosen parameter sets and should not be interpreted as quantitative predictions.

Source Data

Extended Data Fig. 11 Deformation mechanism maps of bridgmanite at 25 GPa/1,900 K (a,c) and 25 GPa/2,500 K (b,d) respectively.

The coloured contours represent the strain rate (s−1) and viscosity (Pa s) in (a, b) and (c, d), respectively. Solid lines distinguish regimes with dominant deformation mechanisms of dislocation creep and diffusion creep. The green (magenta) vertical bars represent the grain size of the shallow lower mantle in the WMO (lowermost mantle in the BMO) and the likely stresses of the mantle convection cores. Parameters used to construct these figures are tabulated in Extended Data Table 1.

Source Data

Extended Data Table 1 Parameters of deformation of bridgmanite
Full size table

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Deng, J., Hu, J., Shi, Y. et al. The potential for bridgmanite megacrysts to drive magma ocean segregation. Nature (2026). https://doi.org/10.1038/s41586-025-10063-5

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