• HOME
  • NEWS
  • EXPLORE
    • CAREER
      • Companies
      • Jobs
    • EVENTS
    • iGEM
      • News
      • Team
    • PHOTOS
    • VIDEO
    • WIKI
  • BLOG
  • COMMUNITY
    • FACEBOOK
    • INSTAGRAM
    • TWITTER
Friday, October 24, 2025
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
  • HOME
  • NEWS
  • EXPLORE
    • CAREER
      • Companies
      • Jobs
        • Lecturer
        • PhD Studentship
        • Postdoc
        • Research Assistant
    • EVENTS
    • iGEM
      • News
      • Team
    • PHOTOS
    • VIDEO
    • WIKI
  • BLOG
  • COMMUNITY
    • FACEBOOK
    • INSTAGRAM
    • TWITTER
No Result
View All Result
Bioengineer.org
No Result
View All Result
Home NEWS Science News Chemistry

Get excited by neural networks

Bioengineer by Bioengineer
June 3, 2020
in Chemistry
Reading Time: 3 mins read
0
IMAGE
Share on FacebookShare on TwitterShare on LinkedinShare on RedditShare on Telegram

IMAGE

Credit: Institute of Industrial Science, The University of Tokyo

Tokyo, Japan – Researchers at the Institute of Industrial Science, The University of Tokyo (UTokyo-IIS) used artificial intelligence to rapidly infer the excited state of electrons in materials. This work can help material scientists study the structures and properties of unknown samples and assist with the design of new materials.

Ask any chemist, and they will tell you that the structures and properties of materials are primarily determined by the electrons orbiting around the molecules that make it up. To be specific, the outermost electrons, which are most accessible for participating in bonding and chemical reactions, are the most critical. These electrons can rest in their lowest energy “ground state,” or be temporarily kicked into a higher orbit called an excited state. Having the ability to predict excited states from ground states would go a long way to helping researchers understand the structures and properties of material samples, and even design new ones.

Now, scientists at UTokyo-IIS have developed a machine learning algorithm to do just that. Using the power of artificial neural networks–which have already proven themselves useful for deciding if your latest credit card transaction was fraudulent or which movie to recommend streaming–the team showed how an artificial intelligence can be trained to infer the excited state spectrum by knowing the ground states of the material.

“Excited states usually have atomic or electronic configurations that are different from their corresponding ground states,” says first author Shin Kiyohara. To perform the training, the scientists used data from core-electron absorption spectroscopy. In this method, a high energy X-ray or electron is used to knock out a core electron orbiting close to the atomic nucleus. Then, the core electron excites to unoccupied orbitals, absorbing the energy of the high energy X-ray/electron. Measuring this energy absorption reveals information about the atomic structures, chemical bonding, and properties of materials.

The artificial neural network took as input the ground state partial density of states, which can be easily computed, and was trained to predict the corresponding excited state spectra. One of the main benefits of using neural networks, as opposed to conventional computational methods, is the ability to apply the results from training set to completely new situations.

“The patterns we discovered for one material showed excellent transferability to others,” says senior author Teruyasu Mizoguchi. “This research in excited states can help scientists better understand chemical reactivity and material function in new or existing compounds.”

###

The work is published in npj Computational Materials as “Learning excited states from ground states by using an artificial neural network” (DOI:10.1038/s41524-020-0336-3)

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 1,000 members including approximately 300 staff and 700 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/3304/

Related Journal Article

http://dx.doi.org/10.1038/s41524-020-0336-3

Tags: Atomic PhysicsAtomic/Molecular/Particle PhysicsChemistry/Physics/Materials SciencesEnergy/Fuel (non-petroleum)GeophysicsIndustrial Engineering/ChemistryMaterialsMolecular PhysicsOpticsPharmaceutical Sciences
Share12Tweet8Share2ShareShareShare2

Related Posts

blank

From Wastewater to Fertile Ground: Chinese Researchers Achieve Dual Breakthroughs in Phosphorus Recycling

October 23, 2025
Innovative ‘Molecular Dam’ Prevents Energy Loss in Nanocrystals

Innovative ‘Molecular Dam’ Prevents Energy Loss in Nanocrystals

October 23, 2025

Physicists Explore Atomic Nuclei Using Innovative Molecule-Based Technique

October 23, 2025

Unlocking Smarter Devices and Safer Drugs: UH Crystals Expert Advances Crystal Formation Control

October 23, 2025
Please login to join discussion

POPULAR NEWS

  • Sperm MicroRNAs: Crucial Mediators of Paternal Exercise Capacity Transmission

    1278 shares
    Share 510 Tweet 319
  • Stinkbug Leg Organ Hosts Symbiotic Fungi That Protect Eggs from Parasitic Wasps

    308 shares
    Share 123 Tweet 77
  • ESMO 2025: mRNA COVID Vaccines Enhance Efficacy of Cancer Immunotherapy

    182 shares
    Share 73 Tweet 46
  • New Study Suggests ALS and MS May Stem from Common Environmental Factor

    132 shares
    Share 53 Tweet 33

About

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

Follow us

Recent News

Key Nervous System Components Found to Regulate Gastrointestinal Tumor Growth

Ruminococcus torques: A Breakthrough in Gut Health

Boosting Hip Fracture Care: Surgery and Mobility Insights

Subscribe to Blog via Email

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

Join 66 other subscribers
  • Contact Us

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
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.