Closed-loop optimization of fast-charging protocols for batteries with machine learning.

Peter M Attia, Aditya Grover, Norman Jin, Kristen A Severson, Todor M Markov, Yang-Hung Liao, Michael H Chen, Bryan Cheong, Nicholas Perkins, Zi Yang, Patrick K Herring, Muratahan Aykol, Stephen J Harris, Richard D Braatz, Stefano Ermon

February 2020 Nature

Synopsis of Social media discussions

Several posts highlight the importance of embracing AI for accelerating Research and Development cycles in sustainable technologies. For instance, one user expressed excitement about using AI to tackle climate change and optimize battery design. Phrases like 'excited about' and references to the transformative potential of AI underscore a positive tone, indicating a strong interest in the topic while maintaining an excitement for innovation.

A
Agreement
 Moderate agreement

Many discussions indicate a general consensus in favor of the findings presented in the publication.

I
Interest
 High level of interest

There is a notable enthusiasm around the application of machine learning in battery technology, reflected in various users expressing their curiosity.

E
Engagement
 Moderate level of engagement

While posts show interest, the engagement level suggests they are primarily sharing resources rather than in-depth analysis.

I
Impact
 High level of impact

Participants in these discussions believe that the publication could significantly affect battery technology and sustainability efforts.

Social Mentions

YouTube

2 Videos

Facebook

3 Posts

Twitter

56 Posts

Blogs

14 Articles

News

28 Articles

Reddit

4 Posts

Metrics

Video Views

36

Total Likes

116

Extended Reach

287,989

Social Features

107

Timeline: Posts about article

Top Social Media Posts

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  • Janamejaya Channegowda
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    March 25, 2023

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    June 19, 2022

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    June 18, 2022

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    I started a new publication - Battery Discovery. I'll review different papers on the Battery Science topic. A new post is on "Closed-loop optimization of fast-charging protocols for batteries with machine learning" (2019) by Attia, Grover et al.: https://t.co/Zi7N09fU1q
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    Next week on Tuesday, 3 PM GMT, we will have Dr. Peter Attia giving a talk on "Closed-loop optimization of fast-charging protocols for #batteries with machine learning" based on his recent @nature paper. https://t.co/rs0VIa3KW5 Here is the link to join: https://t.co/MSc4bZVQLl
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Abstract Synopsis

  • Researchers face bottlenecks in scientific experiments, particularly in optimizing lithium-ion battery design parameters due to time constraints and the need for extensive testing.
  • A new machine learning methodology has been developed to optimize fast-charging protocols for batteries by predicting battery cycle life early and using Bayesian optimization to reduce the number of required experiments.
  • This approach allows for the successful identification of effective charging protocols in just 16 days, significantly quicker than traditional exhaustive search methods, and can be applied to other fields with similar experimental challenges.