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.
In this summary, you will learn
- What kinds of energy materials scientists are looking for,
- Why machine learning can ease this search and
- Which methods improve machine learning for materials discovery.
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.