Learning Multiscale Dynamics of Nanoparticle Self-Assembly
Abstract
Nanoparticle self-assembly offers a powerful route to programmable functional materials, but its dynamics remain difficult to understand because they arise from multiscale, many-body, and environment-dependent interactions. Liquid-phase transmission electron microscopy (LPTEM) enables real-time observation of these dynamics, yet extracting the underlying physical laws from highly stochastic trajectories remains a significant challenge. Here we develop a physics-informed, time-varying autoregressive framework that learns multiscale self-assembly dynamics directly from LPTEM videos. Continuous Eulerian fields with local density and orientational order are coarse-grained from individual video frames and evolved by a sparse, nearest-neighbor operator optimized over a constrained matrix manifold. This interpretable approach captures long-time large scale phase transitions by tracking operator volatility over time, and short-timescale local evolution and transport rules. Crucially, we extract the underlying partial differential equations, where expected symmetries naturally emerge, enabling the direct inference of fundamental coefficients. Our framework provides a robust, computationally efficient tool for discovering hidden physical mechanisms from observational data, opening a path toward the predictive design of self-assembled nanostructures.
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BibTeX
@misc{li2026learning,
title = {{Learning Multiscale Dynamics of Nanoparticle Self-Assembly}},
author = {Shuaifeng Li and Puquan Pan and Jonas Hallstrom and Nicholas Kotov and Paul Bogdan and Qian Chen and Xiaoming Mao},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.36498},
eprint = {2610.36498},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.36498},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Learning Multiscale Dynamics of Nanoparticle Self-Assembly
AU - Shuaifeng Li
AU - Puquan Pan
AU - Jonas Hallstrom
AU - Nicholas Kotov
AU - Paul Bogdan
AU - Qian Chen
AU - Xiaoming Mao
PY - 2026
DA - 2026/10/27
PB - arXiv
DO - 10.48550/arxiv.2610.36498
UR - https://arxiv.org/abs/2610.36498
AB - Nanoparticle self-assembly offers a powerful route to programmable functional materials, but its dynamics remain difficult to understand because they arise from multiscale, many-body, and environment-dependent interactions. Liquid-phase transmission electron microscopy (LPTEM) enables real-time observation of these dynamics, yet extracting the underlying physical laws from highly stochastic trajectories remains a significant challenge. Here we develop a physics-informed, time-varying autoregressive framework that learns multiscale self-assembly dynamics directly from LPTEM videos. Continuous Eulerian fields with local density and orientational order are coarse-grained from individual video frames and evolved by a sparse, nearest-neighbor operator optimized over a constrained matrix manifold. This interpretable approach captures long-time large scale phase transitions by tracking operator volatility over time, and short-timescale local evolution and transport rules. Crucially, we extract the underlying partial differential equations, where expected symmetries naturally emerge, enabling the direct inference of fundamental coefficients. Our framework provides a robust, computationally efficient tool for discovering hidden physical mechanisms from observational data, opening a path toward the predictive design of self-assembled nanostructures.
ER -