• HOME
  • NEWS
    • BIOENGINEERING
    • SCIENCE NEWS
  • EXPLORE
    • CAREER
      • Companies
      • Jobs
    • EVENTS
    • iGEM
      • News
      • Team
    • PHOTOS
    • VIDEO
    • WIKI
  • BLOG
  • COMMUNITY
    • FACEBOOK
    • FORUM
    • INSTAGRAM
    • TWITTER
  • CONTACT US
Monday, March 8, 2021
BIOENGINEER.ORG
No Result
View All Result
  • Login
  • HOME
  • NEWS
    • BIOENGINEERING
    • SCIENCE NEWS
  • EXPLORE
    • CAREER
      • Companies
      • Jobs
        • Lecturer
        • PhD Studentship
        • Postdoc
        • Research Assistant
    • EVENTS
    • iGEM
      • News
      • Team
    • PHOTOS
    • VIDEO
    • WIKI
  • BLOG
  • COMMUNITY
    • FACEBOOK
    • FORUM
    • INSTAGRAM
    • TWITTER
  • CONTACT US
  • HOME
  • NEWS
    • BIOENGINEERING
    • SCIENCE NEWS
  • EXPLORE
    • CAREER
      • Companies
      • Jobs
        • Lecturer
        • PhD Studentship
        • Postdoc
        • Research Assistant
    • EVENTS
    • iGEM
      • News
      • Team
    • PHOTOS
    • VIDEO
    • WIKI
  • BLOG
  • COMMUNITY
    • FACEBOOK
    • FORUM
    • INSTAGRAM
    • TWITTER
  • CONTACT US
No Result
View All Result
Bioengineer.org
No Result
View All Result
Home NEWS Science News

Machine learning aids in simulating dynamics of interacting atoms

Bioengineer by Bioengineer
February 23, 2021
in Science News
0
Share on FacebookShare on TwitterShare on LinkedinShare on RedditShare on Telegram

Automated approach transformative for computational materials science

IMAGE

Credit: Los Alamos National Laboratory

LOS ALAMOS, N.M., February 23, 2021–A revolutionary machine-learning (ML) approach to simulate the motions of atoms in materials such as aluminum is described in this week’s Nature Communications journal. This automated approach to “interatomic potential development” could transform the field of computational materials discovery.

“This approach promises to be an important building block for the study of materials damage and aging from first principles,” said project lead Justin Smith of Los Alamos National Laboratory. “Simulating the dynamics of interacting atoms is a cornerstone of understanding and developing new materials. Machine learning methods are providing computational scientists new tools to accurately and efficiently conduct these atomistic simulations. Machine learning models like this are designed to emulate the results of highly accurate quantum simulations, at a small fraction of the computational cost.”

To maximize the general accuracy of these machine learning models, he said, it is essential to design a highly diverse dataset from which to train the model. A challenge is that it is not obvious, a priori, what training data will be most needed by the ML model. The team’s recent work presents an automated “active learning” methodology for iteratively building a training dataset.

At each iteration, the method uses the current-best machine learning model to perform atomistic simulations; when new physical situations are encountered that are beyond the ML model’s knowledge, new reference data is collected via expensive quantum simulations, and the ML model is retrained. Through this process, the active learning procedure collects data regarding many different types of atomic configurations, including a variety of crystal structures, and a variety of defect patterns appearing within crystals.

###

The paper: Automated discovery of a robust interatomic potential for aluminum, Nature Communications, DOI: 10.1038/s41467-021-21376-0

The funding: This work was funded in part by the Los Alamos National Laboratory Advanced Simulation and Computing (ASC) program and computer time was provided by the Lawrence Livermore National Laboratory Sierra Supercomputer during its open access period.

About Los Alamos National Laboratory

Los Alamos National Laboratory, a multidisciplinary research institution engaged in strategic science on behalf of national security, is managed by Triad, a public service oriented, national security science organization equally owned by its three founding members: Battelle Memorial Institute (Battelle), the Texas A&M University System (TAMUS), and the Regents of the University of California (UC) for the Department of Energy’s National Nuclear Security Administration.

Los Alamos enhances national security by ensuring the safety and reliability of the U.S. nuclear stockpile, developing technologies to reduce threats from weapons of mass destruction, and solving problems related to energy, environment, infrastructure, health, and global security concerns.
LA-UR-21-21717

Media Contact
Nancy Ambrosiano
[email protected]

Original Source

https://www.lanl.gov/discover/news-release-archive/2021/February/0223-machine-learning.php

Related Journal Article

http://dx.doi.org/10.1038/s41467-021-21376-0

Tags: Chemistry/Physics/Materials SciencesComputer ScienceMaterialsTechnology/Engineering/Computer Science
Share12Tweet8Share2ShareShareShare2

Related Posts

IMAGE

Helping people understand glaucoma with a mobile app

March 8, 2021
IMAGE

Virtual avatar coaching with community context for adult-child dyads

March 8, 2021

New Lancet series shows mixed progress on maternal and child undernutrition in last decade

March 7, 2021

“Magic sand” might help us understand the physics of granular matter

March 6, 2021

Leave a Reply Cancel reply

Your email address will not be published.

This site uses Akismet to reduce spam. Learn how your comment data is processed.

POPULAR NEWS

  • IMAGE

    Terahertz accelerates beyond 5G towards 6G

    694 shares
    Share 278 Tweet 174
  • People living with HIV face premature heart disease and barriers to care

    86 shares
    Share 34 Tweet 22
  • Global analysis suggests COVID-19 is seasonal

    39 shares
    Share 16 Tweet 10
  • HIV: an innovative therapeutic breakthrough to optimize the immune system

    36 shares
    Share 14 Tweet 9

About

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

Follow us

Tags

BiologyEcology/EnvironmentMaterialsMedicine/HealthClimate ChangePublic HealthCell BiologyInfectious/Emerging DiseasescancerGeneticsTechnology/Engineering/Computer ScienceChemistry/Physics/Materials Sciences

Recent Posts

  • Helping people understand glaucoma with a mobile app
  • Virtual avatar coaching with community context for adult-child dyads
  • New Lancet series shows mixed progress on maternal and child undernutrition in last decade
  • “Magic sand” might help us understand the physics of granular matter
  • Contact Us

© 2019 Bioengineer.org - Biotechnology news by Science Magazine - Scienmag.

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

© 2019 Bioengineer.org - Biotechnology news by Science Magazine - Scienmag.

Welcome Back!

Login to your account below

Forgotten Password?

Create New Account!

Fill the forms below to register

All fields are required. Log In

Retrieve your password

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

Log In