Learning the diffusion of nanoparticles in liquid phase TEM via physics-informed generative AI

IF 14.7 1区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Zain Shabeeb, Naisargi Goyal, Pagnaa Attah Nantogmah, Vida Jamali
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引用次数: 0

Abstract

The motion of nanoparticles in complex environments can provide us with a detailed understanding of interactions occurring at the molecular level. Liquid phase transmission electron microscopy (LPTEM) enables us to probe and capture the dynamic motion of nanoparticles directly in their native liquid environment, offering real time insights into nanoscale motion and interaction. However, linking motion to interactions to decode the underlying mechanisms of motion and interpret interactive forces at play is challenging, particularly when closed-form Langevin-based equations are not available to model the motion. Herein, we present LEONARDO, a deep generative model that leverages a physics-informed loss function and an attention-based transformer architecture to learn the stochastic motion of nanoparticles in LPTEM. We demonstrate that LEONARDO successfully captures statistical properties suggestive of the heterogeneity and viscoelasticity of the liquid cell environment surrounding the nanoparticles.

Abstract Image

基于生成式人工智能的液相透射电镜纳米粒子扩散研究
纳米粒子在复杂环境中的运动可以为我们提供在分子水平上发生的相互作用的详细理解。液相透射电子显微镜(ltem)使我们能够直接探测和捕捉纳米颗粒在其天然液体环境中的动态运动,提供对纳米级运动和相互作用的实时洞察。然而,将运动与相互作用联系起来以解码运动的潜在机制并解释相互作用的力量是具有挑战性的,特别是当封闭形式的基于朗格万的方程不可用于模拟运动时。在此,我们提出了LEONARDO,这是一个深度生成模型,利用物理信息损失函数和基于注意力的变压器架构来学习LPTEM中纳米颗粒的随机运动。我们证明,LEONARDO成功地捕获了表明纳米颗粒周围液体细胞环境的异质性和粘弹性的统计特性。
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来源期刊
Nature Communications
Nature Communications Biological Science Disciplines-
CiteScore
24.90
自引率
2.40%
发文量
6928
审稿时长
3.7 months
期刊介绍: Nature Communications, an open-access journal, publishes high-quality research spanning all areas of the natural sciences. Papers featured in the journal showcase significant advances relevant to specialists in each respective field. With a 2-year impact factor of 16.6 (2022) and a median time of 8 days from submission to the first editorial decision, Nature Communications is committed to rapid dissemination of research findings. As a multidisciplinary journal, it welcomes contributions from biological, health, physical, chemical, Earth, social, mathematical, applied, and engineering sciences, aiming to highlight important breakthroughs within each domain.
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