Successful application of machine learning in the discovery of new polymers

July 19, 2019

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."
This work was conducted as part of the "Materials Research by Information Integration" Initiative (MI2I), an open innovation accelerator selected by the Japan Science and Technology Agency (JST) as a support program for starting up innovation hub and implemented by NIMS.

Tokyo Institute of Technology

Related Fuel Cells Articles from Brightsurf:

Fuel cells for hydrogen vehicles are becoming longer lasting
An international research team led by the University of Bern has succeeded in developing an electrocatalyst for hydrogen fuel cells which, in contrast to the catalysts commonly used today, does not require a carbon carrier and is therefore much more stable.

Scientists develop new material for longer-lasting fuel cells
New research suggests that graphene -- made in a specific way -- could be used to make more durable hydrogen fuel cells for cars

AI could help improve performance of lithium-ion batteries and fuel cells
Imperial College London researchers have demonstrated how machine learning could help design lithium-ion batteries and fuel cells with better performance.

Engineers develop new fuel cells with twice the operating voltage as hydrogen
Engineers at the McKelvey School of Engineering at Washington University in St.

Iodide salts stabilise biocatalysts for fuel cells
Contrary to theoretical predictions, oxygen inactivates biocatalysts for energy conversion within a short time, even under a protective film.

Instant hydrogen production for powering fuel cells
Researchers from the Chinese Academy of Sciences, Beijing and Tsinghua University, Beijing investigate real-time, on-demand hydrogen generation for use in fuel cells, which are a quiet and clean form of energy.

Ammonia for fuel cells
Researchers at the University of Delaware have identified ammonia as a source for engineering fuel cells that can provide a cheap and powerful source for fueling cars, trucks and buses with a reduced carbon footprint.

Microorganisms build the best fuel efficient hydrogen cells
With billions of years of practice, nature has created the most energy efficient machines.

Atomically precise models improve understanding of fuel cells
Simulations from researchers in Japan provide new insights into the reactions occurring in solid-oxide fuel cells by using realistic atomic-scale models of the electrode active site based on microscope observations instead of the simplified and idealized atomic structures employed in previous studies.

New core-shell catalyst for ethanol fuel cells
Scientists at Brookhaven Lab and the University of Arkansas have developed a highly efficient catalyst for extracting electrical energy from ethanol, an easy-to-store liquid fuel that can be generated from renewable resources.

Read More: Fuel Cells News and Fuel Cells Current Events is a participant in the Amazon Services LLC Associates Program, an affiliate advertising program designed to provide a means for sites to earn advertising fees by advertising and linking to