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

Hong Kong – CE signs Improving Electoral System (Consolidated Amendments) Ordinance 2021 (with photos)

CE signs Improving Electoral System (Consolidated Amendments) Ordinance 2021 (with photos)

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     ​The Chief Executive, Mrs Carrie Lam, today (May 29) signed in accordance with Article 48(3) of the Basic Law the Improving Electoral System (Consolidated Amendments) Ordinance 2021 passed by the Legislative Council (LegCo). The Ordinance will come into immediate effect after it is published in the Gazette on Monday (May 31).

     “Signing bills passed by the LegCo and promulgating laws is one of the Chief Executive’s constitutional powers and functions. I have exercised this power and discharged this function in respect of four legal instruments within a year, which are essential to upholding the principle of ‘One Country, Two Systems’ in the Hong Kong Special Administrative Region (HKSAR) and ensuring its full and faithful implementation. It is indeed a significant responsibility,” Mrs Lam said.

     The four pieces of legislation comprise the National Anthem Ordinance signed on June 11, 2020; the Law of the People’s Republic of China on Safeguarding National Security in the Hong Kong Special Administrative Region, which was passed by the Standing Committee of the National People’s Congress and implemented following the signing of the promulgation by the Chief Executive on June 30, 2020; the Public Offices (Candidacy and Taking Up Offices)(Miscellaneous Amendments) Ordinance 2021 signed on May 20, 2021; and the Ordinance signed today. 

     “The vitality of the law lies in its faithful and accurate implementation. The HKSAR Government will fulfil its responsibility and take resolute enforcement action without fear to strive to safeguard the constitutional order of the HKSAR and ensure its long-term prosperity and stability.

     “I express my gratitude to the LegCo for enacting the several pieces of legislation and various sectors and the general public for their support for the legislative work,” Mrs Lam said.

     Looking ahead, Mrs Lam said that the HKSAR Government’s priorities after completing the legislative work of the Ordinance are fighting COVID-19 to achieve “zero infection”; preparing for the three forthcoming elections in accordance with the law to ensure that they are held in a fair, just and open manner; and reviving the economy and working in concert with the LegCo to resolve the fundamental livelihood issues.