A model that rapidly searches through large numbers of materials could find sustainable alternatives to existing
composites.
TSUKUBA, Japan, May 25, 2022 - (ACN Newswire) - Researchers from Konica Minolta and the Nara Institute of Science and
Technology in Japan have developed a machine learning method to identify sustainable alternatives for composite
materials. Their findings were published in the journal Science and Technology of Advanced Materials: Methods.
Composite materials are compounds made of two or more constituent materials. Due to the complex nature of the
interactions between the different components, their performance can greatly exceed that of single materials. Composite
materials, such as fibre-reinforced plastics, are very important for a wide range of industries and applications,
including electrical and information technologies.
In recent years, there has been increasing demand for more environmentally sustainable materials that help reduce
industrial waste and plastic use. One way to achieve this is to substitute the constituent materials in composites with
recyclable materials or biomass. However, this can reduce performance compared to the original material, not only due to
the features of the individual constituent materials, such as their physicochemical properties, but also due to the
interactions between the constituents.
"Finding a new composite material that achieves the same performance as the original using human experience and
intuition alone takes a very long time because you have to evaluate countless materials while also taking into account
the interactions between them," explains Michihiro Okuyama, assistant manager at Konica Minolta, Inc.
Machine learning offers a potential solution to this problem. Scientists have proposed several machine learning methods
to conduct rapid searches among a large number of materials, based on the relationship between the materials' features
and performance. However, in many cases the properties of the constituent materials are unknown, making these types of
predictive searches difficult.
To overcome this limitation, the researchers developed a new type of machine learning method for finding alternative
materials. A key advantage of the new method is that it can quantitatively evaluate the interactions among the component
materials to reveal how much they contribute to the overall performance of the composite. The method then searches for
replacement constituents with similar performance to the original material.
The researchers tested their method by searching for alternative constituent materials for a composite consisting of
three materials - resin, a filler and an additive. They experimentally evaluated the performance of the substitute
materials identified by machine learning and found that they were similar to the original material, proving that the
model works.
"In developing alternatives, that make up composite materials, our new machine learning method removes the need to test
large numbers of candidates by trial and error, saving both time and money." says Okuyama.
The method could be used to quickly and efficiently identify sustainable substitutes for composite materials, reducing
plastic use and encouraging the use of biomass or renewable materials.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