• HOME
  • NEWS
  • EXPLORE
    • CAREER
      • Companies
      • Jobs
    • EVENTS
    • iGEM
      • News
      • Team
    • PHOTOS
    • VIDEO
    • WIKI
  • BLOG
  • COMMUNITY
    • FACEBOOK
    • INSTAGRAM
    • TWITTER
Tuesday, October 7, 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

Perimeter Institute researchers apply machine learning to condensed matter physics

Bioengineer by Bioengineer
February 13, 2017
in Science News
Reading Time: 2 mins read
0
Share on FacebookShare on TwitterShare on LinkedinShare on RedditShare on Telegram

February 13, 2017 (Waterloo, Ontario, Canada) – A machine learning algorithm designed to teach computers how to recognize photos, speech patterns, and hand-written digits has now been applied to a vastly different set of data: identifying phase transitions between states of matter.

This new research, published today in Nature Physics by two Perimeter Institute researchers, was built on a simple question: could industry-standard machine learning algorithms help fuel physics research? To find out, former Perimeter Institute postdoctoral fellow Juan Cassasquilla and Roger Melko, an Associate Faculty member at Perimeter and Associate Professor at the University of Waterloo, repurposed Google's TensorFlow, an open-source software library for machine learning, and applied it to a physical system.

Melko says they didn't know what to expect. "I thought it was a long shot," he admits.

Using gigabytes of data representing different state configurations created using simulation software on supercomputers, Carrasquilla and Melko created a large collection of "images" to introduce into the machine learning algorithm (also known as a neural network). The result: the neural network distinguished phases of a simple magnet, and could distinguish an ordered ferromagnetic phase from a disordered high-temperature phase. It could even find the boundary (or phase transition) between phases, says Carrasquilla, who now works at quantum computing company D-Wave Systems.

"Once we saw that they worked, then we knew they were going to be useful for many related problems. All of a sudden, the sky's the limit," Melko says. "Everyone like me who has access to massive amounts of data can try these standard neural networks."

This research, which was originally published as a preprint on the arXiv in May, 2016, shows that applying machine learning to condensed matter and statistical physics could open entirely new opportunities for research and, eventually, real-world application.

###

The full journal article can be found here: http://www.nature.com/nphys/journal/vaop/ncurrent/full/nphys4035.html

ABOUT PERIMETER INSTITUTE

Perimeter Institute is the world's largest research hub devoted to theoretical physics. The independent Institute was founded in 1999 to foster breakthroughs in the fundamental understanding of our universe, from the smallest particles to the entire cosmos. Research at Perimeter is motivated by the understanding that fundamental science advances human knowledge and catalyzes innovation, and that today's theoretical physics is tomorrow's technology. Located in the Region of Waterloo, the not-for-profit Institute is a unique public-private endeavour, including the Governments of Ontario and Canada, that enables cutting-edge research, trains the next generation of scientific pioneers, and shares the power of physics through award-winning educational outreach and public engagement.

FOR MORE INFORMATION, CONTACT:

Eamon O'Flynn
Manager, Media Relations
eoflynn[at]perimeterinstitute[dot]ca
(519) 569-7600 x5071
@Perimeter

Media Contact

Eamon O'Flynn
[email protected]
519-569-7600
@perimeter

http://www.perimeterinstitute.ca

############

Story Source: Materials provided by Scienmag

Share12Tweet8Share2ShareShareShare2

Related Posts

Machine Learning Predicts Live Birth Outcomes in IVF

October 7, 2025
Biochar Derived from Invasive Weeds Protects Rice Crops from Toxic Nanoplastics and Heavy Metals

Biochar Derived from Invasive Weeds Protects Rice Crops from Toxic Nanoplastics and Heavy Metals

October 7, 2025

Natural ‘Battery’ of Soil Bacteria and Minerals Dismantles Antibiotics in Darkness

October 7, 2025

Rice University Unveils Second Cohort of Chevron Energy Graduate Fellows

October 7, 2025
Please login to join discussion

POPULAR NEWS

  • Sperm MicroRNAs: Crucial Mediators of Paternal Exercise Capacity Transmission

    939 shares
    Share 375 Tweet 235
  • New Study Reveals the Science Behind Exercise and Weight Loss

    99 shares
    Share 40 Tweet 25
  • New Study Indicates Children’s Risk of Long COVID Could Double Following a Second Infection – The Lancet Infectious Diseases

    95 shares
    Share 38 Tweet 24
  • Ohio State Study Reveals Protein Quality Control Breakdown as Key Factor in Cancer Immunotherapy Failure

    77 shares
    Share 31 Tweet 19

About

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

Follow us

Recent News

Machine Learning Predicts Live Birth Outcomes in IVF

Biochar Derived from Invasive Weeds Protects Rice Crops from Toxic Nanoplastics and Heavy Metals

Natural ‘Battery’ of Soil Bacteria and Minerals Dismantles Antibiotics in Darkness

Subscribe to Blog via Email

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

Join 63 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.