Skip to content
DraconiticResearch
Search Sources API
197,788 studies31 sourcesUpdated 19 min ago
PreprintCC BYChemRxiv

Unveiling Phase Transformation Mechanism in BN from Large-Scale Machine Learning Molecular Dynamics

  • Jaeyoung Cho1,2
  • Nikhil Rampal3,4,5
  • Stephen E. Weitzner3,4,5
  • Graeme Henkelman1,2,6
  • Liwen F. Wan3,4,5
6 affiliations
  1. 1Department of Chemistry
  2. 2University of Texas at Austin
  3. 3Laboratory for Energy Applications for the Future (LEAF
  4. 4Lawrence Livermore National Laboratory
  5. 5Materials Science Division
  6. 6Oden Institute for Computational Engineering & Sciences
Open at sourceDOICite

Abstract

BN-based materials in modern electronics, quantum photonics, and energy devices. Despite their technological importance, the atomistic mechanisms underlying its phase evolution remain poorly understood. Here, we use large-scale molecular dynamics simulations with a MACE-based machine-learning interatomic potential to investigate phase-transformation pathways in crystalline BN. Trained on high-fidelity density functional theory (DFT) data and validated against ab initio molecular dynamics simulations, the potential enables simulations of phase evolution across extended length and time scales. We identify a layer-by-layer transformation mechanism that initiates at interfaces, where lattice mismatch and local structural disorder promote reconstructive transformations. We further show that transformation propensity depends non-monotonically on interfacial energy and is enhanced by compressive stresses generated during thermal treatment. These results provide atomistic insight into BN phase transformations under diverse thermodynamic conditions, while the associated MACE training workflow offers a generalizable framework for studying phase-transformation mechanisms in other complex materials.

Related

  1. Boundary-driven phase transformation in layered 3R In2Se3 captured by long range machine learning molecular dynamics
    Igor Evangelista, Michaela Cohen, Atul C. Thakur +4
    ChemRxivPreprintCC BY-NC-NDFull textMaterials & polymersChemRxiv
  2. Supramolecular Metallohydrogels of Fe(III) and Zn(II) with 3-Aminopropan-1-ol as Low Molecular Weight Hydrogelator: Unveiling Self-healing activity, rheological dynamics, and stimuli-sensitivity
    Tripti Dolai, Subhasish Ghosh, Suresh Kumar Yatirajula +1
    ChemRxivPreprintCC BYFull textMaterials & polymersChemRxiv
  3. Ab Initio Molecular Dynamics with Explicit Solvation Reveals the Volmer–Heyrovsky Mechanism for Chlorine Evolution on Dimensionally Stable Anodes
    Zijin Chen, Jiejie Li, Ziqi Tian
    CatalystsPeer-reviewedCC BY 4.0Electrochemistry & energyCrossref
  4. Crystallographic Origins of Temperature-Dependent Strain Rate Sensitivity and Deformation Mechanism Transitions at TATB/F2314 Interfaces: A Molecular Dynamics Study
    Wendi Shi, Tingting Zhou, Yao Long +3
    PolymersPeer-reviewedCC BY 4.0Materials & polymersCrossref
  5. Molecular Dynamics Insight into Softening Mechanism of Zirconia and Prediction of Removal Depth in Magnetic Abrasive Finishing
    Zhuoyang Wu, Haiji Yang, Jun Ying +3
    ACS OmegaPeer-reviewedCC BY-NC-ND 4.0Full textGeneral chemistryCrossref

Cite

BibTeXRIS
BibTeX
@misc{cho2026unveiling,
  title = {{Unveiling Phase Transformation Mechanism in BN from Large-Scale Machine Learning Molecular Dynamics}},
  author = {Jaeyoung Cho and Nikhil Rampal and Stephen E. Weitzner and Graeme Henkelman and Liwen F. Wan},
  howpublished = {ChemRxiv},
  year = {2026},
  month = sep,
  doi = {10.26434/chemrxiv.15008201/v1},
  url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008201/v1},
  note = {CC BY},
}
RIS
TY  - UNPB
TI  - Unveiling Phase Transformation Mechanism in BN from Large-Scale Machine Learning Molecular Dynamics
AU  - Jaeyoung Cho
AU  - Nikhil Rampal
AU  - Stephen E. Weitzner
AU  - Graeme Henkelman
AU  - Liwen F. Wan
PY  - 2026
DA  - 2026/09/02
PB  - ChemRxiv
DO  - 10.26434/chemrxiv.15008201/v1
UR  - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008201/v1
AB  - BN-based materials in modern electronics, quantum photonics, and energy devices. Despite their technological importance, the atomistic mechanisms underlying its phase evolution remain poorly understood. Here, we use large-scale molecular dynamics simulations with a MACE-based machine-learning interatomic potential to investigate phase-transformation pathways in crystalline BN. Trained on high-fidelity density functional theory (DFT) data and validated against ab initio molecular dynamics simulations, the potential enables simulations of phase evolution across extended length and time scales. We identify a layer-by-layer transformation mechanism that initiates at interfaces, where lattice mismatch and local structural disorder promote reconstructive transformations. We further show that transformation propensity depends non-monotonically on interfacial energy and is enhanced by compressive stresses generated during thermal treatment. These results provide atomistic insight into BN phase transformations under diverse thermodynamic conditions, while the associated MACE training workflow offers a generalizable framework for studying phase-transformation mechanisms in other complex materials.
ER  -