• HOME
  • NEWS
  • EXPLORE
    • CAREER
      • Companies
      • Jobs
    • EVENTS
    • iGEM
      • News
      • Team
    • PHOTOS
    • VIDEO
    • WIKI
  • BLOG
  • COMMUNITY
    • FACEBOOK
    • INSTAGRAM
    • TWITTER
  • CONTACT US
Thursday, February 2, 2023
BIOENGINEER.ORG
No Result
View All Result
  • Login
  • HOME
  • NEWS
  • EXPLORE
    • CAREER
      • Companies
      • Jobs
        • Lecturer
        • PhD Studentship
        • Postdoc
        • Research Assistant
    • EVENTS
    • iGEM
      • News
      • Team
    • PHOTOS
    • VIDEO
    • WIKI
  • BLOG
  • COMMUNITY
    • FACEBOOK
    • INSTAGRAM
    • TWITTER
  • CONTACT US
  • HOME
  • NEWS
  • EXPLORE
    • CAREER
      • Companies
      • Jobs
        • Lecturer
        • PhD Studentship
        • Postdoc
        • Research Assistant
    • EVENTS
    • iGEM
      • News
      • Team
    • PHOTOS
    • VIDEO
    • WIKI
  • BLOG
  • COMMUNITY
    • FACEBOOK
    • INSTAGRAM
    • TWITTER
  • CONTACT US
No Result
View All Result
Bioengineer.org
No Result
View All Result
Home NEWS Science News Chemistry

Bonding’s next top model — Projecting bond properties with machine learning

Bioengineer by Bioengineer
July 19, 2021
in Chemistry
Reading Time: 3 mins read
0
Share on FacebookShare on TwitterShare on LinkedinShare on RedditShare on Telegram

IMAGE

Credit: Institute of Industrial Science, the University of Tokyo

Tokyo, Japan – Designing materials that have the necessary properties to fulfill specific functions is a challenge faced by researchers working in areas from catalysis to solar cells. To speed up development processes, modeling approaches can be used to predict information to guide refinements. Researchers from The University of Tokyo Institute of Industrial Science have developed a machine learning model to determine characteristics of bonded and adsorbed materials based on parameters of the individual components. Their findings are published in Applied Physics Express.

Factors such as the length and strength of bonds in materials play crucial roles in determining the structures and properties we experience on the macroscopic scale. The ability to easily predict these characteristics is therefore valuable when designing new materials.

The density of states (DOS) is a parameter that can be calculated for individual atoms, molecules, and materials. Put simply, it describes the options available to the electrons that arrange themselves in a material. A modeling approach that can take this information for selected components and produce useful data for the desired product–with no need to make and analyze the material–is an attractive tool.

The researchers used a machine learning approach–where the model refines its response without human intervention–to predict four different properties of products from the DOS information of the individual components. Although the DOS has been used as a descriptor to establish single parameters before, this is the first time multiple different properties have been predicted.

“We were able to quantitatively predict the binding energy, bond length, number of covalent electrons, and the Fermi energy after bonding for three different general types of system,” explains study first author Eiki Suzuki. “And our predictions were very accurate across all of the properties.”

Because the calculation of DOS of an isolated state is less complex than for bonded systems, the analysis is relatively efficient. In addition, the neural network model used performed well even when only 20% of the dataset was used for training.

“A significant advantage of our model is that it is general and can be applied to a wide variety of systems,” study corresponding author Teruyasu Mizoguchi explains. “We believe that our findings could make a significant contribution to numerous development processes, for example in catalysis, and could be particularly useful in newer research areas such as nano clusters and nanowires.”

###

The article, “Accurate Prediction of Bonding Properties by a Machine Learning-based Model using Isolated States Before Bonding”, was published in Applied Physics Express at DOI: 10.35848/1882-0786/ac083b.

About Institute of Industrial Science (IIS), the University of Tokyo

Institute of Industrial Science (IIS), the University of Tokyo is one of the largest university-attached research institutes in Japan.

More than 120 research laboratories, each headed by a faculty member, comprise IIS, with more than 1,200 members including approximately 400 staff and 800 students actively engaged in education and research. Our activities cover almost all the areas of engineering disciplines. Since its foundation in 1949, IIS has worked to bridge the huge gaps that exist between academic disciplines and real-world applications.

Media Contact
Teruyasu Mizoguchi
[email protected]

Original Source

https://www.iis.u-tokyo.ac.jp/en/news/3607/

Related Journal Article

http://dx.doi.org/10.35848/1882-0786/ac083b

Tags: Algorithms/ModelsAtomic/Molecular/Particle PhysicsChemistry/Physics/Materials SciencesComputer ScienceElectrical Engineering/ElectronicsMaterialsNanotechnology/MicromachinesResearch/Development
Share12Tweet8Share2ShareShareShare2

Related Posts

University of Houston researchers Chandra Mohan and Richard Willson

Early diagnosis and monitoring of lupus nephritis – on your smartphone

February 1, 2023
Assistant Professor Jo Philips

Uncovering the secrets of electron-eating microorganisms

February 1, 2023

Anna Lee appointed AIP Foundation Executive Director

February 1, 2023

First solid scientific evidence that Vikings brought animals to Britain

February 1, 2023
Please login to join discussion

POPULAR NEWS

  • Jean du Terrail, Senior Machine Learning Scientist at Owkin

    Nature Medicine publishes breakthrough Owkin research on the first ever use of federated learning to train deep learning models on multiple hospitals’ histopathology data

    65 shares
    Share 26 Tweet 16
  • First made-in-Singapore antibody-drug conjugate (ADC) approved to enter clinical trials

    58 shares
    Share 23 Tweet 15
  • Metal-free batteries raise hope for more sustainable and economical grids

    41 shares
    Share 16 Tweet 10
  • One-pot reaction creates versatile building block for bioactive molecules

    37 shares
    Share 15 Tweet 9

About

We bring you the latest biotechnology news from best research centers and universities around the world. Check our website.

Follow us

Recent News

Tuberculosis vaccine does not protect elderly against COVID-19

Flue2Chem: Science-based industries join forces for first time to address UK net zero targets

What’s that sound? Automobile horn changed history and communications technology

Subscribe to Blog via Email

Enter your email address to subscribe to this blog and receive notifications of new posts by email.

Join 42 other subscribers
  • Contact Us

Bioengineer.org © Copyright 2023 All Rights Reserved.

No Result
View All Result
  • Homepages
    • Home Page 1
    • Home Page 2
  • News
  • National
  • Business
  • Health
  • Lifestyle
  • Science

Bioengineer.org © Copyright 2023 All Rights Reserved.

Welcome Back!

Login to your account below

Forgotten Password?

Retrieve your password

Please enter your username or email address to reset your password.

Log In