Click here to learn about Academy events, publications and initiatives around COVID-19.

We are experiencing intermittent technical difficulties. At this time, you may not be able to log in, register for an event, or make a donation via the website. We appreciate your patience, and apologize for any inconvenience this may cause.

Our site is under planned maintenance. At this time, you will not be able to log in, register for an event, or make a donation via the website. We appreciate your patience, and apologize for any inconvenience this may cause.

Support The World's Smartest Network
×

Help the New York Academy of Sciences bring late-breaking scientific information about the COVID-19 pandemic to global audiences. Please make a tax-deductible gift today.

DONATE
This site uses cookies.
Learn more.

×

This website uses cookies. Some of the cookies we use are essential for parts of the website to operate while others offer you a better browsing experience. You give us your permission to use cookies, by continuing to use our website after you have received the cookie notification. To find out more about cookies on this website and how to change your cookie settings, see our Privacy policy and Terms of Use.

We encourage you to learn more about cookies on our site in our Privacy policy and Terms of Use.

eBriefing

Advances in AI for Materials

Advances in AI for Materials
Reported by
Liang Dong and Jennifer L. Costley

Posted December 02, 2020

Presented By

The New York Academy of Sciences

Previous conferences and workshops covering artificial intelligence (AI) for Materials Science have mainly focused on introducing AI into materials simulations, which is only the first step in new materials discovery. These efforts have largely ignored AI’s promise for materials synthesis and translating research into high-volume industrial production.

On October 6-7, 2020, the New York Academy of Sciences hosted the AI for Materials symposium to provide a broader perspective on leveraging the benefits of AI in material simulations, experiments, and development efforts for high volume production. The symposium brought together materials scientists, industry experts, and AI researchers to cover the application of AI throughout the entire life cycle of new materials, from lab discovery to industrial production. These leaders also shape future research directions, identify urgent issues in this rising field, and foster interdisciplinary collaboration opportunities.


In This eBriefing, You’ll Learn

  • How machine learning is being applied to understand the physical processes behind materials science
  • Approaches to improve the data infrastructures used in materials science research to facilitate easier integration and promote a better data sharing environment
  • How AI is being applied to address industry-related issues in materials science, including the scalability of materials production from the lab to the factory and the synthetic and catalytic routes of new materials
Physics and Causality in Machine Learning
Data Infrastructures for Materials Science
AI in Materials Production and Industry
Automating Production from Lab to Factory