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SUMMARY:Harvesting the Sun: using AI to design better materials for energy
  - Xiaolei Feng\, University of Cambridge\, Department of Earth Sciences
DTSTART:20200218T120000Z
DTEND:20200218T131500Z
UID:TALK137266@talks.cam.ac.uk
CONTACT:Jonathan Rosser
DESCRIPTION:Chair: Alex Archibald\nAbstract: Our Sun provides almost all t
 he energy that society depends upon. The first industrial revolution\, dri
 ven by the discovery that steam made by burning coal provides the energy t
 o drive machines\, has led to the warming planet that we must adapt to tod
 ay. But even that first fuel\, that fossil fuel\, was itself generated fro
 m the Sun\, by photosynthesis in plants hundreds of millions of years ago.
  Plants continue to use photosynthesis to make the energy that we all rely
  on for food\, directly or indirectly via other animals\, for our daily su
 rvival. But the fourth industrial of machine learning and artificial intel
 ligence is now providing a route to designing and discovering new material
 s that harvest the Sun’s energy directly. We are familiar with silicon p
 hotovoltaics (PV) in solar cells\, for generating electricity from sunligh
 t. Now\, new materials offer more convenient\, cheaper\, and more efficien
 t routes to harvesting the light that shines upon us daily. Computing powe
 r opens new ways to search for new materials\, and to predict which will w
 ork best. I will discuss how computer predictions of new materials provide
  a route to designing better routes to harvest energy from the Sun\, in su
 stainable and effective devices. Maybe silicon\, in the shape of computer 
 processors and banks of memory\, will be the means by which we can build a
  sustainable future from materials that first emerge\, in silico\, rather 
 than in the test tube or crucible. That is our aim.
LOCATION:Bullard Lab\, Seminar Room
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