AI Creates periodic table of elements within hours
Stanford scientists have developed a new artificial intelligence programme that organised the periodic table of elements in just a few hours, a feat that took mankind nearly a century of trial and error.
Called Atom2Vec, the program successfully learned to distinguish between different atoms after analysing 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.
"We wanted to know whether an AI can be smart enough to discover the periodic table on its own, and our team showed that it can," said Shou-Cheng Zhang, from Stanford University in the US. The research, published in the journal Proceedings of the National Academy of Sciences, is an important first step toward a more ambitious goal of his, which is designing a replacement to the Turing test, the current gold standard for gauging machine intelligence. In order for an AI to pass the Turing test, it must be capable of responding to written questions in ways that are indistinguishable from a human. However, Zhang thinks the test is flawed because it is subjective.
"Humans are the product of evolution and our minds are cluttered with all sorts of irrationalities. For an AI to pass the Turing test, it would need to reproduce all of our human irrationalities," Zhang said. "That's very difficult to do, and not a particularly good use of programmers time," he said.
Zhang would instead like to propose a new benchmark of machine intelligence. "We want to see if we can design an AI that can beat humans in discovering a new law of nature. But in order to do that, we first have to test whether our AI can make some of the greatest discoveries already made by humans," he said. By recreating the periodic table of elements, Atom2Vec has achieved this secondary goal, Zhang said.
Researchers modelled 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 analysing the vectors, the AI can estimate the probability of a word appearing in a text given the co-occurrence of other words.
For example, the word "king" is often accompanied by "queen," and "man" by "woman." Thus, the mathematical vector of "king" might be translated roughly as "king = a queen minus a woman plus a man. We can apply the same idea to atoms. Instead of feeding in all of the words and sentences from a collection of texts, we fed Atom2Vec all the known chemical compounds, such as NaCl, KCl, H2O, and so on," Zhang said.
From this sparse data, the AI program figured out, for example, that potassium (K) and sodium (Na) must have similar properties because both elements can bind with chlorine (Cl). Zhang hopes that in the future, scientists can harness Atom2Vec's knowledge to discover and design new materials.