Abstract
Silicon carbide (SiC) is an important technological material, but its high-temperature phase diagram has remained unclear due to conflicting experimental results about congruent versus incongruent melting. Here, we employ large-scale machine learning molecular dynamics (MLMD) simulations to gain insights into SiC decomposition and phase transitions. Our approach relies on a Bayesian active learning workflow to efficiently train an accurate machine learning force field on density functional theory data. Our large-scale simulations provide direct indication that melting of SiC proceeds incongruently via decomposition into silicon-rich and carbon phases at high temperature and pressure. During cooling at high pressures, carbon nanoclusters nucleate and grow within the homogeneous molten liquid. During heating, the decomposed mixture reversibly transitions back into a homogeneous SiC liquid. The full pressure-temperature phase diagram of SiC is systematically constructed using MLMD simulations, providing new understanding of the nature of phases, resolving long-standing inconsistencies from previous experiments and yielding technologically relevant implications for processing and deposition of this material.
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Data availability
The data and scripts are available on Zenodo: https://doi.org/10.5281/zenodo.14648292 and https://doi.org/10.5281/zenodo.15066527.
Code availability
Post-processing scripts are available on Github: https://github.com/YuuuXie/SiC_MLMD_phase_diagram. For the machine learning force field, this work utilizes FLARE (version 1.3.0) for training and deployment, available at https://github.com/mir-group/flare.
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Acknowledgements
We acknowledge Cameron Owen and Anders Johansson for discussions and help with the large simulation setup and computational resources. We acknowledge Evelyn Hu for helpful discussions and feedback. Y.X. is supported by the "Design & Assembly of Atomically-Precise Quantum Materials & Devices" grant DE-SC0020128 of the Department of Energy. M.W. is supported by the National Science Foundation, Office of Advanced Cyberinfrastructure (OAC), under Award No. 2118201. B.K. and F.S. are supported by the Harvard University Materials Research Science and Engineering Center funded by the National Science Foundation grant DMR-2011754. The simulation and analysis are done on the Harvard Cannon cluster. This research used resources of the National Energy Research Scientific Computing Center (NERSC), a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231 using NERSC award BES-ERCAP0024206.
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Y.X. initiated the project, performed the training of the ML force field, phase transition simulations and post-analysis. M.W. contributed to the dataset and code preparation, phase transition simulations and post-analysis. S.R. contributed to the DFT settings and the collection of experimental results. F.S. guided the analysis of nucleation and decomposition. B.K. supervised all aspects of the project. All authors contributed to the writing of the manuscript.
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Xie, Y., Wang, M., Ramakers, S. et al. Incongruent melting and phase diagram of SiC from machine learning molecular dynamics. npj Comput Mater (2026). https://doi.org/10.1038/s41524-026-01976-4
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DOI: https://doi.org/10.1038/s41524-026-01976-4
