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Hardware Science

Computer Prediction Used to Design Better Organic Semiconductors 32

An anonymous reader writes "Creating a flexible display requires finding an organic material that's both durable and capable of carrying an electric signal fast enough. To create such a material requires choosing the right compound and combining it with an organic base material. It's a hit and miss affair that can take years of synthesis to get right, but even then the final material may not be good enough. Stanford and Harvard researchers have come up with a much faster solution: use computer prediction to decide on the best compound before synthesizing begins. They also proved it works by developing a new organic semiconductor material 30x faster than the amorphous silicon used in LCDs."
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Computer Prediction Used to Design Better Organic Semiconductors

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  • by JoshuaZ ( 1134087 ) on Thursday August 18, 2011 @05:59PM (#37135570) Homepage

    Over time, there have been more and more ways of getting computers to our scientific work for us. There's been success with this sort of thing for a long time. For example, in math the mid 1990s the Robbins conjecture [] was proven using essentially an automated theorem proving system. Similarly, Simon Colton has done work with computer systems that can notice patterns and make novel mathematical definitions and conjectures without human input ahref= []>. There's been work in other fields as well such as in automated experiment systems in biochemistry [].

    One interesting thing about TFA is that it is suggesting that powerful enough systems might be able purely through simulation to predict what new compounds are worth investigating. In principle, this could lead eventually to self-improving systems where a system designs better and better algorithms and hardware for itself which it uses to then design even better ones and so on. This sort of Singularity scenario generally seems implausible to me but it is one of the more plausible Singularity scenarios and articles like this make me wonder if it should be taken seriously. Obviously, there are serious limits on that sort of thing, since one runs into the laws of physics and the laws of mathematics eventually. You can't improve algorithms beyond a certain point (you start running into P!=NP sort of issues). And you can't improve physical efficiency beyond the laws of physics. In essence, even if I can look through a search space a 1000 times as fast, that doesn't mean I will find a solution that is a 1000 times as good. But with access to both software and hardware improvements, substantial improvement may be possible.

    Of course, at the current stage this is highly limited. The simulations in question in their current forms can at best target specific molecules that look promising. They are very far from actually predicting the exact behavior. Indeed, my old college roomate is a physicist who works on computational physics and related issues, and one thing he and a few others have been working on for a long time is trying to derive accurate behavior for water from quantum mechanical principles. This is still not doable beyond a rough approximation. The computations are simply too difficult. And as TFA notes, figuring out how to synthesize new compounds can still take years. So this is a really interesting result, but it isn't something that is likely to see immediate impact. Still very cool though.

APL hackers do it in the quad.