Artificial intelligence is aiding the search for energy materials
- PDF / 919,661 Bytes
- 2 Pages / 585 x 783 pts Page_size
- 38 Downloads / 206 Views
•
Energy Sector Analysis
The combination of big data and AI is being called the “fourth industrial revolution,” and its applications in materials science have soared in the past decade. The AI subfield of machine learning is already aiding the discovery of new materials.
Artificial intelligence is aiding the search for energy materials By Prachi Patel Feature Editor: Shyue Ping Ong
E
arly on, there was the incandescent light bulb. Now, buildings and streets are lit by light-emitting diodes (LEDs) that use less than 25% of the energy and last 25 times longer. The LED is the result of painstaking years spent searching for the right semiconducting compounds and finessing their microstructures. And it’s just one example of how materials science is pivoting the transition to a future where everyone has access to sustainable, affordable energy. Satiating the needs and wants of an increasingly wealthy, digitized world will require new ways to convert and use energy. Materials are the foundation of those advances. As commercial silicon solar cells reach their theoretical limit, complementing them with efficient, affordable perovskite solar cells could help photovoltaic modules generate more power. The search is intensifying for advanced batteries that pack more energy than lithium-ion devices for lower cost and do not use scarce, expensive metals such as cobalt. Researchers are also investigating better thermoelectric materials, catalysts for clean energy technologies, and porous materials that soak up carbon. Yet materials development today is still mostly a result of intuition and luck. The empirical process is slow and fraught with human bias and error. Artificial intelligence (AI) is poised to change that. The combination of big data and AI is being called the “fourth industrial revolution,” and its applications in materials science have soared in the past decade. Researchers have already used the AI subfield of machine learning to find new battery electrode materials and phosphors for solar cells. Identifying and developing a material for a technological application usually takes more than a decade. AI could slash that to one or two years. Speeding up materials discovery is not the main driver for the use of AI in materials research, though. According to Gerbrand Ceder, professor of materials science and engineering at the University of California, Berkeley, “AI is best for solving problems that we don’t understand and things that we don’t know how to predict. Materials synthesis is a perfect example. A researcher could think of a new compound for a catalyst or electrode material, said Ceder, “but we don’t have any predictive theory to tell us how to make the compound or if it can exist and be made at all. People have historical knowledge or intuition, and have made hundreds of thousands of compounds. Machine learning could find patterns in these data and learn the rules of synthesis.”
Materials scientists have used computational techniques for decades. Ab initio computing, for instance, simulates the behavior
Data Loading...
