Summary of Use Machine Learning to Find Energy Materials

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The world’s need for energy is increasing. Because current energy technology has limited efficiency, the search is on for improved materials. Machine learning – the ability of a computer system to improve its routines by analyzing data – can rapidly predict the potential of new materials for energy applications. Graduate students Phil De Luna and Jennifer Wei, as well as professors Yoshua Bengio, Alán Aspuru-Guzik, and Edward Sargent suggest how to help. getAbstract recommends this article to anyone looking for better energy technologies.

About the Authors

Phil De Luna is a graduate student in Materials Science and Engineering at the University of Toronto. Jennifer Wei is a graduate student in Chemistry and Chemical Biology at Harvard University. Yoshua Bengio is a professor of Computer Science and Operations Research at Université Montréal, Canada. Alán Aspuru-Guzik is a professor of Chemistry and Chemical Biology at Harvard University. Edward Sargent is a professor of Electrical and Computer Engineering at the University of Toronto, Canada.

 

Summary

Why are scientists searching for new energy materials?

The world’s need for energy continues to grow. Governments and corporations are spending billions of dollars to find new materials. They are searching for materials that are more efficient, less expensive, safer, more durable, and scalable than existing ones.

How can scientists speed up the search for new energy materials?

There is a glut of materials with the potential for use in energy technologies. Testing the properties of even a few is laborious...


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