Get intelligentvalue.com

Own it today or select a payment plan

Buy Now$3,879Payment Plan$325/mo

Secured by Stripe

Premium Domain Name

intelligentvalue.com

is available for purchase

179 views

Visitors from

US 54%·

AU 32%·

IN 7%·

GB 2%·

FR 2%

Perfect for

Financial Consulting Technology Startups Educational Services Economic Forecasting E-commerce Solutions Business Analytics Artificial Intelligence Solutions Investment Advisory Strategic Planning Market Research

Unlock the potential of 'intelligentvalue.com', a premium domain that embodies sophistication and expertise in investment advisory and financial consulting. Perfect for businesses in artificial intelligence solutions, market research, and strategic planning, this memorable domain conveys a strong branding message that resonates with clients seeking innovative and data-driven insights. Elevate your presence in the competitive landscape with a digital identity that signifies intelligence, value, and forward-thinking solutions.

Safe & Secure

Protected transactions with Stripe

Fast Transfer

Domain transferred within 24 hours

Flexible Payments

Interest-free payment plans available

Machine learning can predict the mechanical properties of polymers

By: Science and Technology of Advanced Materials via ACN Newswire

October 25, 2024 at 10:00 AM EDT

ⓘ This article is third-party content and does not represent the views of this site. We make no guarantees regarding its accuracy or completeness.

TSUKUBA, Japan, Oct 25, 2024 - (ACN Newswire) - Polymers such as polypropylene are fundamental materials in the modern world, found in everything from computers to cars. Because of their ubiquity, it’s vital that materials scientists know exactly how each newly developed polymer will perform under different preparation conditions. Thanks to a new study, which was published in Science and Technology of Advanced Materials, scientists can now use machine learning to determine what to expect from a new polymer.

Machine learning predicts the material properties of new polymers with high accuracy, providing a nondestructive alternative to conventional polymer testing methods.

Predicting the mechanical properties of new polymers, such as their tensile strength or flexibility, usually involves putting them through destructive and costly physical tests. However, a team of researchers from Japan, led by Dr. Ryo Tamura, Dr. Kenji Nagata, and Dr. Takashi Nakanishi from the National Institute for Materials Science in Tsukuba, showed that machine learning can predict the material properties of polymers. They developed the method on a group of polymers called homo-polypropylenes, using X-ray diffraction patterns of the polymers under different preparation conditions to provide detailed information about their complex structure and features.

“Machine learning can be applied to data from existing materials to predict the properties of unknown materials,” Drs. Tamura, Nagata, and Nakanishi explain. “However, to achieve accurate predictions, it’s essential to use descriptors that correctly represent the features of these materials.”

Thermoplastic crystalline polymers, such as polypropylene, have a particularly complex structure that is further altered during the process of molding them into the shape of the end product. It was, therefore, important for the team to adequately capture the details of the polymers’ structure with X-ray diffraction and to ensure that the machine learning algorithm could identify the most important descriptors in that data.

The new method accurately captured the structural changes of commonly used plastic Polypropylene during the molding process into the end product.

To that end, they analysed two datasets using a tool called Bayesian spectral deconvolution, which can extract patterns from complex data. The first dataset was X-ray diffraction data from 15 types of homo-polypropylenes subjected to a range of temperatures, and the second was data from four types of homo-polypropylenes that underwent injection molding. The mechanical properties analysed included stiffness, elasticity, the temperature at which the material starts to deform, and how much it would stretch before breaking.

The team found that the machine learning analysis accurately linked features in the X-ray diffraction imagery with specific material properties of the polymers. Some of the mechanical properties were easier to predict from the X-ray diffraction data, while others, such as the stretching break point, were more challenging.

“We believe our study, which describes the procedure used to provide a highly accurate machine learning prediction model using only the X-ray diffraction results of polymer materials, will offer a nondestructive alternative to conventional polymer testing methods,” the NIMS researchers say.

The team also suggested that their Bayesian spectral deconvolution approach could be applied to other data, such as X-ray photoelectron spectroscopy, and used to understand the properties of other materials, both inorganic and organic.

“It could become a test case for future data-driven approaches to polymer design and science,” the NIMS team says.

Further information
Ryo Tamura
National Institute for Materials Science (NIMS)
tamura.ryo@nims.go.jp

Kenji Nagata
National Institute for Materials Science (NIMS)
nagata.kenji@nims.go.jp

Takashi Nakanishi
National Institute for Materials Science (NIMS)
nakanishi.takashi@nims.go.jp

Paper: https://doi.org/10.1080/14686996.2024.2388016

About Science and Technology of Advanced Materials (STAM)

Open access journal STAM publishes outstanding research articles across all aspects of materials science, including functional and structural materials, theoretical analyses, and properties of materials. https://www.tandfonline.com/STAM

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.

Report this content

If you believe this article contains misleading, harmful, or spam content, please let us know.

Report this article

Symbol	Price	Change (%)
AMZN	264.14	-3.08 (-1.15%)
AAPL	300.23	+2.02 (0.68%)
AMD	424.10	-25.60 (-5.69%)
BAC	49.77	-0.08 (-0.16%)
GOOG	393.32	-3.85 (-0.97%)
META	614.23	-4.20 (-0.68%)
MSFT	421.92	+12.49 (3.05%)
NVDA	225.32	-10.42 (-4.42%)
ORCL	192.95	-2.66 (-1.36%)
TSLA	422.24	-21.06 (-4.75%)