|
| Tuesday, 27 November 2018, 20:30 JST | |
| |
|
|
|
|
| - Trials in polymer design processes can be cut to 1/40 by utilizing AI. |
TOKYO, Nov 27, 2018 - (JCN Newswire) - Showa Denko (SDK), National Institute of Advanced Industrial Science and Technology (AIST), and Research Association of High-Throughput Design and Development for Advanced Functional Materials (ADMAT) have found that the number of trials for the design of polymers with desired properties can be cut to about one-fortieth by utilizing artificial intelligence (AI).
 | | Figure 1: Concept of evaluation of number of trials for polymer design/verification |
 | | Figure 2: Demonstration of high efficiency of AI-based polymer search |
 | | Figure 3: Building AI |
Researchers from SDK, AIST, and ADMAT conducted AI-based searches for polymers with desired properties, aiming to demonstrate the effectiveness of AI technology in the process of polymer design. As a model case, they focused on glass transition temperature, an index of heat resistance. Using 417 different types of structural data on polymers with known structures and glass transition temperatures, they conducted an AI-based search for a polymer with the highest glass transition temperature to see whether it is possible to shorten the development cycle.
First, randomly selected 10 sets of data were supplied as training data for AI. The Extended Connectivity Circular Fingerprints (ECFP) method was applied to the training data, digitizing structural features of polymers. Then, using Bayesian optimization*, the researchers made repeated efforts to predict and verify a polymer with the highest glass transition temperature out of the remaining 407 sets of data. Thus, they checked the number of trials required until the discovery of a target polymer. (Figure 1). To prevent the influence of the choice of data on the results, 500 examinations were conducted with different sets of initial training data, and the average number of trials was evaluated.
As a result, the researchers succeeded in discovering a target polymer with the highest glass transition temperature with an extremely small number of trials, namely, 4.6 times of trials on the average (Figure 2). This figure is about one-fortieth of the number of trials required under random selection of polymers, confirming the effectiveness of AI-based polymer design.
To build AI, it is necessary to convert the features of polymers into numerical values. By applying ECFP (a method for representing monomer structures) to this development work, the researchers found repeating units of molecules (such as functional groups) extracted automatically and structural features expressed appropriately in numerical vectors (Figure 3). Using AI built on these data, the researchers realized high-precision property predictions at the speed of 0.25 second per polymer. Thus, it became possible to make comprehensive property predictions for a large number of candidate polymers within a limited amount of time.
Furthermore, by using Bayesian optimization in prediction, the researchers discovered a polymer with the highest glass transition temperature out of about 400 types of candidate polymers, based on 13.6 sets of training data.
Prior to this study, it was believed that AI-based predictions with a small number of training data would be less accurate, and that a large amount of training data would be required. However, this development work suggests that AI would be effective in solving problems even in the area of developing most-advanced functional materials, where only a limited amount of training data seems to be available.
*[Bayesian optimization: A method for selecting the next candidate material to be examined by considering estimated prediction error, in addition to predicted values.]
We will continue working to improve the technology in order to utilize it in actual development of advanced functional materials. A detailed presentation regarding this development work will be made at 2018 MRS Fall Meeting scheduled for November 27 (U.S. Eastern time).
This development work has been subcontracted by NEDO (New Energy and Industrial Technology Development Organization) Ultra High-Throughput Design and Prototyping Technology for Ultra Advanced Materials Development Project (Ultra-Ultra PJ: P16010). By fully utilizing multiscale simulation and AI, Ultra-Ultra PJ aims to reduce the length of time required for development to one-twentieth compared with conventional ways of material development based on knowledge and experience.
Contact:
SDK
Public Relations Office
Phone: +81-3-5470-3235
Fax: +81-3-3431-6215
AIST
Media Relations Office
Phone: +81-29-862-6216
E-mail: press-ml@aist.go.jp
ADMAT
S. Hatakeyama
Phone: +81-29-856-3580
E-mail: s-hatakeyama@admat.or.jp
Topic: Press release summary
Sectors: Chemicals, Spec.Chem, Science & Nanotech, Engineering, Automation [IoT]
https://www.acnnewswire.com
From the Asia Corporate News Network
Copyright © 2024 ACN Newswire. All rights reserved. A division of Asia Corporate News Network.
|
|
|
|
|
|
|
| |
Latest Press Releases
Explore the Next Era of DevOps at Exito's DevOps Summit in Dubai 
Nov 14, 2024 14:04 HKT/SGT
|
|
|
Disney Dream Reimagined! New Little Mermaid Wedding Ring Collection
Nov 14, 2024 14:00 HKT/SGT
|
|
|
Academic Labs, the Pioneering Crypto Platform Achieves a $300M at Its ICO
Nov 14, 2024 09:00 HKT/SGT
|
|
|
Blueberry Rebrand Reflects Exciting New Era for the Forex Company
Nov 14, 2024 09:00 HKT/SGT
|
|
|
ULVAC Launches Oil Rotary Vacuum Pump Gv135
Thursday, November 14, 2024 9:00:00 AM
|
|
|
Femto Technologies Announces Third Quarter 2024 Financial Results
Nov 14, 2024 07:00 HKT/SGT
|
|
|
Ethics Board Approval Granted for Lexaria's 12-Week Phase 1b DehydraTECH GLP-1 Study in Diabetes and Weight Loss
Nov 13, 2024 23:00 HKT/SGT
|
|
|
Interview with Spectral's Sean Brehm on DQLDB the Vogon Cloud
Nov 13, 2024 20:40 HKT/SGT
|
|
|
Spectral Releases its Quantum Bridge Strategy for a Decentralized, Sustainable Future Using Quantum-Resilient Edge Computing
Nov 13, 2024 20:32 HKT/SGT
|
|
|
COP29: PLN Encourages Global Collaboration for Energy Transition Towards Sustainable Energy Self-Sufficiency
Nov 13, 2024 20:00 HKT/SGT
|
|
|
|
|
More Press release >> |
|
|
|
