Sunday, April 11, 2021
  • Setup menu at Appearance » Menus and assign menu to Top Bar Navigation
Advertisement
  • AI Development
    • Artificial Intelligence
    • Machine Learning
    • Neural Networks
    • Learn to Code
  • Data
    • Blockchain
    • Big Data
    • Data Science
  • IT Security
    • Internet Privacy
    • Internet Security
  • Marketing
    • Digital Marketing
    • Marketing Technology
  • Technology Companies
  • Crypto News
No Result
View All Result
NikolaNews
  • AI Development
    • Artificial Intelligence
    • Machine Learning
    • Neural Networks
    • Learn to Code
  • Data
    • Blockchain
    • Big Data
    • Data Science
  • IT Security
    • Internet Privacy
    • Internet Security
  • Marketing
    • Digital Marketing
    • Marketing Technology
  • Technology Companies
  • Crypto News
No Result
View All Result
NikolaNews
No Result
View All Result
Home Machine Learning

Building a better battery with machine learning and artificial intelligence

November 29, 2019
in Machine Learning
Building a better battery with machine learning and artificial intelligence
586
SHARES
3.3k
VIEWS
Share on FacebookShare on Twitter



ANI |
Updated:
Nov 29, 2019 14:01 IST

Washington D.C. [USA], Nov 29 (ANI): With the help of machine learning and artificial intelligence researchers are accelerating the power of batteries.
Researchers at the U.S. Department of Energy’s (DOE) Argonne National Laboratory have turned to the power of machine learning and artificial intelligence to dramatically accelerate the process of battery discovery, according to the study published in — Chemical Science.
As described in two new papers, Argonne researchers first created a highly accurate database of roughly 133,000 small organic molecules that could form the basis of battery electrolytes.
To do so, they used a computationally intensive model called G4MP2. This collection of molecules, however, represented only a small subset of 166 billion larger molecules that scientists wanted to probe for electrolyte candidates.
Because using G4MP2 to resolve each of the 166 billion molecules would have required an impossible amount of computing time and power, the research team used a machine-learning algorithm to relate the precisely known structures from the smaller data set to much more coarsely modelled structures from the larger data set.
“When it comes to determining how these molecules work, there are big tradeoffs between accuracy and the time it takes to compute a result,” said Ian Foster, Argonne Data Science and Learning division director and author of one of the papers. “We believe that machine learning represents a way to get a molecular picture that is nearly as precise at a fraction of the computational cost.”
To provide a basis for the machine learning model, Foster and his colleagues used a less computationally taxing modelling framework based on density functional theory, a quantum mechanical modelling framework used to calculate electronic structure in large systems.
Density functional theory provides a good approximation of molecular properties, but is less accurate than G4MP2.
Refining the algorithm to better ascertain information about the broader class of organic molecules involved comparing the atomic positions of the molecules computed with the highly accurate G4MP2 versus those analyzed using only density functional theory.
By using G4MP2 as a gold standard, the researchers could train the density functional theory model to incorporate a correction factor, improving its accuracy while keeping computational costs down.
“The machine learning algorithm gives us a way to look at the relationship between the atoms in a large molecule and their neighbours, to see how they bond and interact, and look for similarities between those molecules and others we know quite well,” said Argonne computational scientist Logan Ward, an author of one of the studies.
“This will help us to make predictions about the energies of these larger molecules or the differences between the low- and high-accuracy calculations,” added Ward.
“This whole project is designed to give us the biggest picture possible of battery electrolyte candidates,” continued Argonne chemist Rajeev Ward, an author of both studies.
“If we are going to use a molecule for energy storage applications, we need to know properties like its stability, and we can use this machine learning to predict properties of bigger molecules more accurately,” added Ward. (ANI)

You might also like

Why Machine Learning Over Artificial Intelligence?

27 million galaxy morphologies quantified and cataloged with the help of machine learning

Machine learning and big data needed to learn the language of cancer and Alzheimer’s


Credit: Google News

Previous Post

Facebook issued online falsehoods directive after Singapore site fails to comply

Next Post

AI Storytelling Companies Usher in New Era of Characters, Relationships

Related Posts

Why Machine Learning Over Artificial Intelligence?
Machine Learning

Why Machine Learning Over Artificial Intelligence?

April 11, 2021
27 million galaxy morphologies quantified and cataloged with the help of machine learning
Machine Learning

27 million galaxy morphologies quantified and cataloged with the help of machine learning

April 11, 2021
Machine learning and big data needed to learn the language of cancer and Alzheimer’s
Machine Learning

Machine learning and big data needed to learn the language of cancer and Alzheimer’s

April 11, 2021
Basic laws of physics spruce up machine learning
Machine Learning

New machine learning method accurately predicts battery state of health

April 11, 2021
Can a Machine Learning Model Predict T2D?
Machine Learning

Can a Machine Learning Model Predict T2D?

April 11, 2021
Next Post
AI Storytelling Companies Usher in New Era of Characters, Relationships

AI Storytelling Companies Usher in New Era of Characters, Relationships

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Recommended

Plasticity in Deep Learning: Dynamic Adaptations for AI Self-Driving Cars

Plasticity in Deep Learning: Dynamic Adaptations for AI Self-Driving Cars

January 6, 2019
Microsoft, Google Use Artificial Intelligence to Fight Hackers

Microsoft, Google Use Artificial Intelligence to Fight Hackers

January 6, 2019

Categories

  • Artificial Intelligence
  • Big Data
  • Blockchain
  • Crypto News
  • Data Science
  • Digital Marketing
  • Internet Privacy
  • Internet Security
  • Learn to Code
  • Machine Learning
  • Marketing Technology
  • Neural Networks
  • Technology Companies

Don't miss it

Why Machine Learning Over Artificial Intelligence?
Machine Learning

Why Machine Learning Over Artificial Intelligence?

April 11, 2021
27 million galaxy morphologies quantified and cataloged with the help of machine learning
Machine Learning

27 million galaxy morphologies quantified and cataloged with the help of machine learning

April 11, 2021
Machine learning and big data needed to learn the language of cancer and Alzheimer’s
Machine Learning

Machine learning and big data needed to learn the language of cancer and Alzheimer’s

April 11, 2021
Job Scope For MSBI In 2021
Data Science

Job Scope For MSBI In 2021

April 11, 2021
Basic laws of physics spruce up machine learning
Machine Learning

New machine learning method accurately predicts battery state of health

April 11, 2021
Can a Machine Learning Model Predict T2D?
Machine Learning

Can a Machine Learning Model Predict T2D?

April 11, 2021
NikolaNews

NikolaNews.com is an online News Portal which aims to share news about blockchain, AI, Big Data, and Data Privacy and more!

What’s New Here?

  • Why Machine Learning Over Artificial Intelligence? April 11, 2021
  • 27 million galaxy morphologies quantified and cataloged with the help of machine learning April 11, 2021
  • Machine learning and big data needed to learn the language of cancer and Alzheimer’s April 11, 2021
  • Job Scope For MSBI In 2021 April 11, 2021

Subscribe to get more!

© 2019 NikolaNews.com - Global Tech Updates

No Result
View All Result
  • AI Development
    • Artificial Intelligence
    • Machine Learning
    • Neural Networks
    • Learn to Code
  • Data
    • Blockchain
    • Big Data
    • Data Science
  • IT Security
    • Internet Privacy
    • Internet Security
  • Marketing
    • Digital Marketing
    • Marketing Technology
  • Technology Companies
  • Crypto News

© 2019 NikolaNews.com - Global Tech Updates