AI Plus a Chemistry Robot Finds All the Reactions That Will Work (arstechnica.com)
A team of researchers at Glasgow University have built a robot that uses machine learning to run and analyze its own chemical reaction. The system is able to figure out every reaction that's possible from a given set of starting materials. Ars Technica reports: Most of its parts are dispersed through a fume hood, which ensures safe ventilation of any products that somehow escape the system. The upper right is a collection of tanks containing starting materials and pumps that send them into one of six reaction chambers, which can be operated in parallel. The outcomes of these reactions can then be sent on for analysis. Pumps can feed samples into an IR spectrometer, a mass spectrometer, and a compact NMR machine -- the latter being the only bit of equipment that didn't fit in the fume hood. Collectively, these can create a fingerprint of the molecules that occupy a reaction chamber. By comparing this to the fingerprint of the starting materials, it's possible to determine whether a chemical reaction took place and infer some things about its products.
All of that is a substitute for a chemist's hands, but it doesn't replace the brains that evaluate potential reactions. That's where a machine-learning algorithm comes in. The system was given a set of 72 reactions with known products and used those to generate predictions of the outcomes of further reactions. From there, it started choosing reactions at random from the remaining list of options and determining whether they, too, produced products. By the time the algorithm had sampled 10 percent of the total possible reactions, it was able to predict the outcome of untested reactions with more than 80-percent accuracy. And, since the earlier reactions it tested were chosen at random, the system wasn't biased by human expectations of what reactions would or wouldn't work. The research has been published in the journal Nature.
All of that is a substitute for a chemist's hands, but it doesn't replace the brains that evaluate potential reactions. That's where a machine-learning algorithm comes in. The system was given a set of 72 reactions with known products and used those to generate predictions of the outcomes of further reactions. From there, it started choosing reactions at random from the remaining list of options and determining whether they, too, produced products. By the time the algorithm had sampled 10 percent of the total possible reactions, it was able to predict the outcome of untested reactions with more than 80-percent accuracy. And, since the earlier reactions it tested were chosen at random, the system wasn't biased by human expectations of what reactions would or wouldn't work. The research has been published in the journal Nature.
And equally important: can it be networked to similar machines (preferably made by other manufacturers and run by different labs) to set up its own peer reviews?
And how soon before the drug cartels are buying up every machine that is produced to discover new substances?
politicians are like babies' nappies: they should both be changed regularly and for the same reasons
I hope it can figure out to make kryptonite! :)
LOL
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I wonder if it's possbile to automatically design an industrial scale chemical process, an entire factory complex?
Say I want to produce x amount of this or that product. What raw materials do we need? Where should we build the factory? What would it cost? What are the options?
The system should of course take into account costs, externalities and regulations. Automatic procurement of commodity materials should be possible as well.
A carefully selected group of these working in parallel could theoretically parse the entire possible set of reactions, given sufficient time. (Yes, I know that with infinite molecular weight, there is an infinite number of possible compounds. However, only so many heavy molecules are interesting or useful, and of those, there will be certain classes that are more interesting than others. This approach would permit investigation of pathways without actually expending reagents, once its models are accurate enough. That means after a certain amount of training, a theoretical molecule of interest could be presented to the AI, and it could shit out the ideal synthesis pathway, and the next efficient arbitrary "n" pathways.)
This is the kind of thing that is the beginning of universal replicators.
why would you use AI with a 80% success rate for this?
isn't chemistry all maths? can't you just calculate the same reactions and have a 100% success rate?
On a long enough timeline, the survival rate for everyone drops to zero.
Be careful what you feed it.
"National Security is the chief cause of national insecurity." - Celine's First Law
This is a prime example of why patents are absurd and should be discontinued.
The skeptic in me wonders if 80% of the combinations had no reaction
Have gnu, will travel.
This is complete garbage. Finding chemical reactions is a search through a high dimensional space. AI algorithms do do such a search. Poorly. The kind of search that is reported involves finding the trivial. Finding something truly new requires a chemist's insight, just like any other creative activity. For example, what catalysts are tried? What are the limits to the ambient pressure?
But it is backwards chemistry. Nobody in industry has a bunch of stuff on the shelf and says 'what can I make with this'. There is a chemical they need to make and they ask themselves 'how can I make this'.
love is just extroverted narcissism
>isn't chemistry all maths? can't you just calculate the same reactions and have a 100% success rate?
Because chemistry is applied quantum mechanics, and the math is HARD. I think they finally managed to simulate the chemical properties of a hydrogen molecule, but last I heard that was the most complicated molecule we've been able to simulate accurately - and it's literally the simplest molecule that exists, by a wide margin.
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You know what they got from those?
Mundane Potions
Have gnu, will travel.