2025/10/10 by M. Li, Lei, YaoKun, Yi Yang +2
Materials Science · #Enzyme Structure and Function #FOS: Physical sciences #Machine Learning in Materials Science #Materials Science (cond-mat.mtrl-sci) #Soft Condensed Matter (cond-mat.soft) #Statistical Mechanics (cond-mat.stat-mech) #X-ray Diffraction in Crystallography
paper · pdf · doi:10.48550/arxiv.2510.08960
openalex publication_date 2025/10/10 · openalex created_date 2025/10/14 · openalex updated_date 2026/07/28
Crystallization, a fundamental phase transition process governing material formation in natural and industrial contexts, involves the spontaneous emergence of long-range structural order from disordered phases. This long-range periodicity involves spatial and molecular orientation order. Molecular dynamics (MD) simulations of crystallization require collective variables (CVs) that accurately distinguish this long-\range periodicity. Existing CVs based on local descriptors (e.g., bond-orientational order) often lack transferability across crystal structures. To address this, we propose a unified CV framework derived from the multipole expansion of Ewald summation: a mathematical formalism bridging X-ray diffraction (XRD) principles and electrostatic energy computation in MD. By projecting atomic configurations onto a basis of spherical harmonics (complete for angular function representation), our CV achieves high-fidelity encoding of both translational and orientational order. Metadynamics simulations demonstrate that this CV drives efficient sampling of polymorphic pathways for known crystals and predicts stable phases even without crystal structures. This approach shows potential as a transferable platform for ab initio crystal structure prediction.