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New framework learns ab initio phase-field models from molecular dynamics

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new framework for learning ab initio phase-field models, which aims to simulate microstructure evolution with both quantum-mechanical accuracy and mesoscopic reach. This method derives the mesoscopic equation from molecular dynamics, with neural networks learning the unspecified free energy and mobility from simulations. The framework has been demonstrated on iron-boron melts and hydrogen-helium mixtures, providing thermodynamic insights and enabling simulations at scales previously unattainable with atomistic modeling. AI

IMPACT Enables more accurate and large-scale simulations for materials science and physics.

RANK_REASON The cluster contains a single academic paper detailing a new computational framework. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework learns ab initio phase-field models from molecular dynamics

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The cluster contains a single academic paper detailing a new computational framework. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Mengyi Chen, Peichen Zhong, Zihan Zhang, Qianxiao Li ·

    Learning ab initio phase-field models

    arXiv:2610.01432v1 Announce Type: cross Abstract: Simulating microstructure evolution requires quantum-mechanical accuracy and mesoscopic reach in length and time scales, a combination that no current method achieves. Classical phase-field models provide this reach, but their acc…

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