Nov 17, 2023 11:00 JST

Source: Science and Technology of Advanced Materials

Machine learning techniques improve X-ray materials analysis
Analysis of materials can be done quicker and with less expertise with the help of proven machine learning techniques established in biomedical fields.

TSUKUBA, Japan, Nov 17, 2023 - (ACN Newswire) - Researchers of RIKEN at Japan’s state-of-the-art synchrotron radiation facility, SPring-8, and their collaborators, have developed a faster and simpler way to carry out segmentation analysis, a vital process in materials science. The new method was published in the journal Science and Technology of Advanced Materials: Methods.

The SPring-8 facility has a storage ring with a circumference of 1.5 km
The SPring-8 facility has a storage ring with a circumference of 1.5 km

Segmentation analysis is used to understand the fine-scale composition of a material. It identifies distinct regions (or ‘segments’) with specific compositions, structural characteristics, or properties. This helps evaluate the suitability of a material for specific functions, as well as its possible limitations. It can also be used for quality control in material fabrication and for identifying points of weakness when analyzing materials that have failed.

Segmentation analysis is very important for synchrotron radiation X-ray computed tomography (SR-CT), which is similar to conventional medical CT scanning but uses intense focused X-rays produced by electrons circulating in a storage ring at nearly the speed of light. The team have demonstrated that machine learning is capable in conducting the segmentation analysis for the refraction contrast CT, which is especially useful for visualizing the three-dimensional structure in samples with small density differences between regions of interest, such as epoxy resins.

“Until now, no general segmentation analysis method for synchrotron radiation refraction contrast CT has been reported,” says first author Satoru Hamamoto. “Researchers have generally had to do segmentation analysis by trial and error, which has made it difficult for those who are not experts.”

The team’s solution was to use machine learning methods established in biomedical fields in combination with a transfer learning technique to finely adjust to the segmentation analysis of SR-CTs. Building on the existing machine learning model greatly reduced the amount of training data needed to get results.

“We’ve demonstrated that fast and accurate segmentation analysis is possible using machine learning methods, at a reasonable computational cost, and in a way that should allow non-experts to achieve levels of accuracy similar to experts,” says Takaki Hatsui, who led the research group.

The researchers carried out a proof-of-concept analysis in which they successfully detected regions created by water within an epoxy resin. Their success suggests that the technique will be useful for analyzing a wide range of materials.

To make this analysis method available as widely and quickly as possible, the team plans to establish segmentation analysis as a service offered to external researchers by the SPring-8 data center, which has recently started its operation.

Further information
Public Relations Office, RIKEN
Tel: 050-3495-0305
Email: ex-press@riken.jp 
2-1 Hirosawa, Wako, Saitama, 351-0198, Japan
https://www.riken.jp/en/ 

Paper: https://doi.org/10.1080/27660400.2023.2270529 

About Science and Technology of Advanced Materials: Methods (STAM-M)

STAM Methods is an open access sister journal of Science and Technology of Advanced Materials (STAM), and focuses on emergent methods and tools for improving and/or accelerating materials developments, such as methodology, apparatus, instrumentation, modeling, high-through put data collection, materials/process informatics, databases, and programming. https://www.tandfonline.com/STAM-M 

Dr Yasufumi Nakamichi
STAM Publishing Director
Email: NAKAMICHI.Yasufumi@nims.go.jp

Press release distributed by Asia Research News for Science and Technology of Advanced Materials.

Source: Science and Technology of Advanced Materials
Sectors: Chemicals, Spec.Chem, Science & Nanotech

Copyright ©2024 ACN Newswire. All rights reserved. A division of Asia Corporate News Network.

Related Press Release


Sensing structure without touching
February 27 2024 08:00 JST
 
Nano-sized probes reveal how cellular structure responds to pressure
November 21 2023 07:00 JST
 
A bio-inspired twist on robotic handling
November 14 2023 20:00 JST
 
GPT-4 artificial intelligence shows some competence in chemistry
October 17 2023 08:00 JST
 
Closing the loop between artificial intelligence and robotic experiments
August 24 2023 09:00 JST
 
Machine intelligence for designing molecules and reaction pathways
May 24 2023 09:00 JST
 
Face-down: Gravity's effects on cell movement
May 13 2023 00:00 JST
 
Polymer protection for vaccines and drugs
December 09 2022 23:00 JST
 
Revealing crystal structures robotically
December 02 2022 20:00 JST
 
New data extracted from old for materials databases
November 07 2022 23:00 JST
 
More Press release >>

Latest Press Release


More Latest Release >>