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Updated: Jan 14, 2026

3D Modeling of Dendritic Spines with Synaptic Plasticity

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Published on: May 18, 2020

Conditional diffusion model for inverse prediction of process parameters and dendritic microstructures from

Arisa Ikeda¹, Ryo Higuchi², Tomohiro Yokozeki²

¹Graduate School of Science and Technology, Keio University, 3-14-1, Hiyoshi, Kohoku-ku, Yokohama, Kanagawa, 223-8522, Japan.

Scientific Reports

|October 23, 2025

Summary

This study introduces a data-driven inverse design method using a conditional diffusion model to predict optimal process parameters and microstructures for desired mechanical properties in materials development, reducing experimental costs.

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Area of Science:

Materials Science
Computational Materials Science
Polymer Science

Background:

Materials development is costly due to extensive experimentation and simulations.
Data-driven inverse design methods offer a cost-effective approach to materials development.
Understanding microstructure-property relationships is crucial for achieving desired mechanical performance.

Purpose of the Study:

To develop a conditional diffusion model for inverse design of materials.
To predict optimal process parameters and microstructures for specific mechanical properties.
To reduce the cost and time associated with materials development.

Main Methods:

Development of a conditional diffusion model.
Application of the model to polymeric materials, specifically matrix resins for carbon fiber reinforced thermoplastics.
Utilizing inverse analysis to predict process parameters and microstructures based on desired mechanical properties.

Main Results:

The trained diffusion model successfully proposes processing temperature and predicts microstructure for given Young's modulus and Poisson's ratio.
The model demonstrates capability in representing complex dendritic microstructures.
The method is applicable to various materials and can handle multiple process parameters and mechanical properties simultaneously.

Conclusions:

Conditional diffusion models provide an efficient data-driven inverse design approach for materials development.
This method significantly reduces the need for extensive experimental trials and simulations.
The developed model offers a versatile tool for optimizing material properties through microstructure and process parameter prediction.