Successful application of machine learning in the discovery of new polymers
As a powerful example of how artificial intelligence (AI) can accelerate the discovery of new materials, scientists in Japan have designed and verified polymers with high thermal conductivity — a property that would be the key to heat management, for example, in the fifth-generation (5G) mobile communication technologies. Their study highlights the great advantages of machine learning methods over traditional ways of searching for high-performance materials.
A joint research group including Ryo Yoshida (Professor and Director of the Data Science Center for Creative Design and Manufacturing at the Institute of Statistical Mathematics [ISM], Research Organization of Information and Systems), Junko Morikawa (Professor at the School of Materials and Chemical Technology, Tokyo Institute of Technology [Tokyo Tech]), and Yibin Xu (Group Leader of Thermal Management and Thermoelectric Materials Group, Center for Materials Research by Information Integration, Research and Services Division of Materials Data and Integrated System [MaDIS], NIMS) has demonstrated the promising application of machine learning (ML) — a form of AI that enables computers to “learn” from given data — for discovering innovative materials.
Reporting their findings in the open-access journal npj Computational Materials, the researchers show that their ML method, involving “transfer learning”, enables the discovery of materials with desired properties even from an exceeding small data set.
The study drew on a data set of polymeric properties from PoLyInfo, the largest database of polymers in the world housed at NIMS. Despite its huge size, PoLyInfo has a limited amount of data on the heat transfer properties of polymers. To predict the heat transfer properties from the given limited data, ML models on proxy properties were pre-trained where sufficient data were available on the related tasks; these pre-trained models captured common features relevant to the target task. Re-purposing such machine-acquired features on the target task yielded outstanding prediction performance even with the exceedingly small datasets, as if highly experienced human experts can make rational inferences even for considerably less experienced tasks. The team combined this model with a specially designed ML algorithm for computational molecular design, which is called the iQSPR algorithm previously developed by Yoshida and his colleagues. Applying this technique enabled the identification of thousands of promising “virtual” polymers.
From this large pool of candidates, three polymers were selected based on their ease of synthesis and processing.
Tests confirmed that the new polymers have a high thermal conductivity of up to 0.41 Watts per meter-Kelvin (W/mK). This figure is 80 percent higher than that of typical polyimides, a group of commonly used polymers that have been mass-produced since the 1950s for applications ranging from fuel cells to cookware.
By verifying the heat transfer properties of the computationally designed polymers, the study represents a key breakthrough for fast, cost-effective, ML-supported methods for materials design. It also demonstrates the team’s combined expertise in data science, organic synthesis and advanced measurement technologies.
Yoshida comments that many aspects remain to be explored, such as “training” computational systems to work with limited data by adding more suitable descriptors. “Machine learning for polymer or soft material design is a challenging but promising field as these materials have properties that differ from metals and ceramics, and are not yet fully predicted by the existing theories,” he says.
The study is a starting point for the discovery of other innovative materials, as Morikawa adds: “We would like to try to create an ML-driven high-throughput computational system to design next-generation soft materials for applications going beyond the 5G era. Through our project, we aim to pursue not only the development of materials informatics but also contribute to fundamental advancement of materials science, especially in the field of phonon engineering.”
The Latest on: New materials development
via Google News
The Latest on: New materials development
- Wheel Barrow Market 2021 Revenue Analysis Focuses On Plastic Material and Aluminum Material, According to Market.uson November 30, 2020 at 1:37 pm
The latest research report provides a complete assessment of the Global Wheel Barrow market for the forecast year 2021-2030, which is beneficial for companies regardless of their size and revenue.
- Amgen Announces New Data Being Presented At ASH 2020on November 30, 2020 at 12:00 pm
THOUSAND OAKS, Calif., Nov. 30, 2020 /PRNewswire/ -- Amgen (NASDAQ:AMGN) today announced several upcoming data presentations from its oncology ...
- New family of quasiparticles appears in grapheneon November 30, 2020 at 9:56 am
Telltale traces Researchers at the University of Manchester in the UK have identified a new family of quasiparticles in superlattices made from graphene sandwiched between two slabs of boron nitride.
- Local company buys plot in Duquesne from RIDC for new headquarterson November 30, 2020 at 9:19 am
Construction and civil engineering firm to build new headquarters building in Duquesne to accommodate growth demands.
- GeoVax Labs Signs License Deal With NIH To Advance Vaccines Developmenton November 30, 2020 at 7:34 am
(RTTNews) - GeoVax Labs Inc. (GOVX, GOVXW) said that it has signed a Patent and Biological Materials License Agreement with the National Institute of Allergy and Infectious Diseases or NIAID, part of ...
- The Most Significant Advanced Materials News from 2020, Revealed by IDTechExon November 30, 2020 at 6:32 am
PRNewswire/ -- 2020 has been one of the most turbulent years in recent history. Not a single country or industry has been ...
- Mitsubishi Heavy Industries Invests in Monolith Materialson November 30, 2020 at 1:08 am
Leader in Innovative Technology for Reducing Environmental Impact- Investment will strengthen and diversify MHI Group's hydrogen value chain through innovative ...
- Speeding up the development of new materialson November 28, 2020 at 11:18 pm
Understanding materials through computer simulations has long been a goal in the material science community. (Courtesy of Los Alamos National Laboratory ) People have been using metals for thousands ...
- Barrier Packaging Materials Market Products, Developments, And Forecast To 2030-Market.Bizon November 23, 2020 at 11:36 pm
Market.Biz :The research report on the Global Barrier Packaging Materials Market delivers extensive analysis of market trends and shares from 2021 to 2030. Which will help to analyzes the current ...
- Anti-microbial Packaging Materials Market Products, Developments, And Forecast To 2030-Market.Bizon November 23, 2020 at 9:13 pm
Market.Biz :The research report on the Global Anti-microbial Packaging Materials Market delivers extensive analysis of market trends and shares from 2021 to 2030. Which will help to analyzes the ...
via Bing News