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Stanford AI recreates chemistry's periodic table of elements
It took nearly a century of trial and error for human scientists to organize the periodic table of elements, arguably one of the greatest scientific achievements in chemistry, into its current form. A new artificial intelligence (AI) program developed by Stanford physicists accomplished the same feat in just a few hours.
Called Atom2Vec, the program successfully learned to distinguish between different atoms after analyzing a list of chemical compound names from an online database. The unsupervised AI then used concepts borrowed from the field of natural language processing – in particular, the idea that the properties of words can be understood by looking at other words surrounding them – to cluster the elements according to their chemical properties.
[...] Zhang and his group modeled Atom2Vec on an AI program that Google engineers created to parse natural language. Called Word2Vec, the language AI works by converting words into numerical codes, or vectors. By analyzing the vectors, the AI can estimate the probability of a word appearing in a text given the co-occurrence of other words.
[...] Zhang hopes that in the future, scientists can harness Atom2Vec's knowledge to discover and design new materials. "For this project, the AI program was unsupervised, but you could imagine giving it a goal and directing it to find, for example, a material that is highly efficient at converting sunlight to energy," Zhang said.
Wake me up when an AI discovers the Island of Stability.
Learning atoms for materials discovery (open, DOI: 10.1073/pnas.1801181115) (DX)
(Score: 1, Interesting) by Anonymous Coward on Thursday June 28 2018, @10:57AM
Most of the times functional domains are combined to make engineered proteins.
From most proteins we know what they more or less do, the difficult part comes when you include cellular localization and protein modifications. A single mutation can could cause a large shift in (one of) these properties. To some degree you could make predictions on how they react, but not always.