Machine learning-enabled exploration of mesoscale architectures in amphiphilic-molecule self-assembly

Aug 3, 2026

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Amphiphilic molecules spontaneously form self-assembled structures whose shapes depend on molecular structure, temperature, and physical conditions. The functional performance of these structures is determined by their formation process, yet evaluating them requires many procedural steps during molecular simulation workflows. The study develops machine-learning models that extract structural features from mesoscale, non-ordered self-assembled structures using particle-type and coordinate data. Graph neural networks learn local structural information and achieve up to 78.35% classification accuracy for systems up to 4,050 coarse-grained particles, enabling efficient structural analysis without manual feature engineering.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages26

Reading Time1 h 6 min

AccessFree to download

Introduction

Background on amphiphilic molecule self-assembly

Simulation approaches and their limitations

Prior quantitative evaluations and motivation

Methodology and proposed ML approach

Graph neural networks for local structural extraction

Training inputs: particle types and coordinate data

Results

Dataset size and system scale

Classification accuracy

Conclusion

Implications for materials science applications

arXiv :2402 . 19019v2 [ cond-mat .soft] 17 Apr 2024 ARTICLE Machine learning-enabled exploration of mesoscale architectures inamphiphilic-molecule self-assembly Takeo Sudoa , Satoki Ishiaia , Yuuki Ishiwataria , Takahiro Yokoyamaa , Kenji Yasuokaa , and Noriyoshi Araia∗ a Department of Mechanical Engineering, Keio University, Yokohama, Japan. ARTICLE HISTORY Compiled April 18, 2024 ABSTRACT Amphiphilic molecules spontaneously form self-assembled structures of various shapes depending on their molecular structures, the temperature, and other physical conditions. The functionalities of these structures are dictated by their formations and their properties must be evaluated for reproduction using molecular simulations. However, the assessment of such intricate structures involves many procedural steps. This study investigates the potential of machine-learning models to extract struc tural features from mesoscale non-ordered self-assembled structures, and suggests a methodology in which machine-learning models for the structural analysis of self assembled structures are trained on particle types and coordinate data. In the pro posed approach, graph neural networks are utilised to extract local structural data for analysis. In simulations using several hundred self-assembled structures of up to 4050 coarse-grained particles, local structures are successfully extracted and classi fied with up to 78 .35% accuracy. As the machine-learning models learn structural characteristics without the need for human-made feature engineering, the proposed method has important potential applications in the field of materials science. KEYWORDS Graph neural network, Self-assembly, Machine learning, structural analysis, dissipative particle dynamics 1. Introduction Amphiphilic molecules are versatile substances used in various industries and products, such as cosmetics, detergents, and pharmaceuticals[1–3] . These molecules are also gaining attention in the fields of materials science, chemical engineering, and medical materials because of their easy-to-control synthesis of chemical structures and their ability to self-assemble; that is, to form distinctive functional structures without human intervention. The functionalities of the self-assembled structures formed by amphiphilic molecules, such as micelles and vesicles, are closely related to their formations. Micelles are commonly used in detergents to render oil-based impurities soluble. Vesicles, including liposomes, are frequently used as pharmaceutical carriers in biological systems[4–6] . Notably, amphiphilic-molecule functionalities are not solely derived from their molecular structures but also from their self-assembled structures. Therefore, predicting and evaluating these structures are essential for realising ∗ Corresponding author. Email: [arai@mech.keio.ac.jp](arai@mech.keio.ac.jp); Fax: +81 45 566 1495; Tel: +81 45 566 1846 highly functional materials and manipulating their functionalities; however, a systematic framework has yet to be established. At present, the development of functional materials using amphiphilic molecules involves repeated experiments based on trial and error. Molecular motion can be simulated by virtually manipulating each atom using a computer, and this approach is gaining attention for its potential to predict the formation of self-assembled structures and their resulting functionalities in soft matter based on molecular structures[7,8] . However, molecular dynamics simulation[9,10], which simulates the motion of each atom in a system, is limited by computational constraints when simulating mesoscale structures such as self-assembled structures in soft matter. Dissipative particle dynamics (DPD) simulation[11–13], which aggregatesa given number of atoms into a single bead and simulates their behaviour collectively, facilitates simulations at such scales. DPD simulations can reproduce self-assembled stru

FAQs

Why is evaluating self-assembled structures of amphiphilic molecules challenging?

Their structures depend on molecular structure and physical conditions, but quantitative assessment through simulation involves many procedural steps and lacks a systematic framework. Complex configurations also cannot be easily evaluated visually.

What is the core idea of the proposed machine-learning method?

The approach trains machine-learning models to extract structural features from mesoscale, non-ordered self-assembled structures using particle types and coordinate data. Graph neural networks are used to capture local structural information for analysis.

How accurate is the structural classification in the simulations?

In simulations across several hundred self-assembled structures with up to 4,050 coarse-grained particles, local structures are successfully extracted and classified with up to 78.35% accuracy.

Aug 3, 2026

Upload by Liam