Unearthing new materials with the aid of machine learning

[PoppaGeek]

Unearthing new materials with the aid of machine learning

Al?n Aspuru-Guzik is building facial recognition software?for molecules.

Aspuru-Guzik, a professor of chemistry at Harvard University, uses computers to explore chemical space?the near-infinite array of molecules that can be created by joining tens or hundreds of atoms together into different shapes. With the help of quantum chemistry calculations, he and his team sift through millions of virtual molecules in search of promising new materials for solar energy generation and storage.

And they are applying machine learning to speed up the process.

?Machine learning is basically a rebranding or a renaming of the old field of artificial intelligence,? said Aspuru-Guzik. ?They have successfully used it to filter spam every day, to do very intelligent translations and image recognition ? so it?s not too hard to think that machine learning could have a very big impact in chemistry.?

Out of the approximately 10⁶⁰ possible molecules that can be formed by linking tens or hundreds of atoms together, how do chemists identify which ones could be useful as new solar cell materials or pharmaceutical drugs?

One approach is to make individual molecules in the lab, and then test to see what they can do. But theoretical chemists like Aspuru-Guzik prefer to simulate molecules in a computer and use quantum calculations to estimate their properties. Once the computer has identified promising candidates, experimentalists can focus their energy on making only those.

?You can make a molecule maybe every ten weeks with your hands, but you can compute it in seconds,? said Aspuru-Guzik at CASW?s New Horizons in Science briefing during the ScienceWriters2014 conference in Columbus, Ohio, on Oct. 19. ?We want to use computers more and more because making [molecules] is so hard.?

As part of the Harvard Clean Energy Project, for instance, Aspuru-Guzik and his team used quantum chemistry calculations to screen 2.3 million molecules for organic solar cells devices and found 35,000 highly-efficient candidates.

But though it is faster and cheaper to simulate a molecule before making it in real life, quantum chemical calculations are still costly in terms of computing time. Aspuru-Guzik estimates that it took over 20,000 CPU years to screen the 2.3 million molecules—that is, it would take a single computer 20,000 years to run all of these calculations.

With the help of the IBM World Community Grid, which relies on donated computer time from a network of volunteers, these calculations were completed in three years. But to accelerate the search, Aspuru-Guzik is using machine learning to rapidly recognize some of the most promising molecules.

More at link.