Machine learning techniques improve X-ray materials analysis
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.
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.
Topic: Press release summary
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Indonesian Machine Learning Startup Tarrasmart Acquired for $75 Million, Paving the Way for Job Creation and Growth in the Country’s Tech Scene
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Improving machine learning for materials design
A new approach can train a machine learning model to predict the properties of a material using only data obtained through simple measurements, saving time and money compared with those currently used. It was designed by researchers at Japan’s National Institute for Materials Science (NIMS), Asahi KASEI Corporation, Mitsubishi Chemical Corporation, Mitsui Chemicals, and Sumitomo Chemical Co and reported in the journal Science and Technology of Advanced Materials: Methods.
“Machine learning is a powerful tool for predicting the composition of elements and process needed to fabricate a material with specific properties,” explains Ryo Tamura, a senior researcher at NIMS who specializes in the field of materials informatics.
A tremendous amount of data is usually needed to train machine learning models for this purpose. Two kinds of data are used. Controllable descriptors are data that can be chosen without making a material, such as the chemical elements and processes used to synthesize it. But uncontrollable descriptors, like X-ray diffraction data, can only be obtained by making the material and conducting experiments on it.
“We developed an effective experimental design method to more accurately predict material properties using descriptors that cannot be controlled,” says Tamura.
The approach involves the examination of a dataset of controllable descriptors to choose the best material with the target properties to use for improving the model’s accuracy. In this case, the scientists interrogated a database of 75 types of polypropylenes to select a candidate with specific mechanical properties.
They then selected the material and extracted some of its uncontrollable descriptors, for example, its X-ray diffraction data and mechanical properties.
This data was added to the present dataset to better train a machine learning model employing special algorithms to predict a material’s properties using only uncontrollable descriptors.
“Our experimental design can be used to predict difficult-to-measure experimental data using easy-to-measure data, accelerating our ability to design new materials or to repurpose already known ones, while reducing the costs,” says Tamura. The prediction method can also help improve understanding of how a material’s structure affects specific properties.
The team is currently working on further optimizing their approach in collaboration with chemical manufacturers in Japan.
Further information
Ryo Tamura
National Institute for Materials Science (NIMS)
Email: tamura.ryo@nims.go.jp
About Science and Technology of Advanced Materials: Methods (STAM Methods)
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. Yoshikazu Shinohara
STAM Methods Publishing Director
Email: SHINOHARA.Yoshikazu@nims.go.jp
Press release distributed by Asia Research News for Science and Technology of Advanced Materials.
Topic: Press release summary
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