1/10/2023 0 Comments Periodic table chemistry nycTshitoyan and colleagues applied these methods to 3.3 million abstracts of papers in materials science published between 19, spanning a vocabulary of half a million ‘words’ – some of which are in fact chemical formulae. Algorithms already exist for analysing texts this way, and have been used previously to look for trends in literature and historical documents, without needing guidance from human supervision. They have used ML to look for correlations between the actual words used in published papers within their materials science dataset. The researchers have turned this problem into an opportunity. Word associations obtained by machine-reading the materials literature also uncovers the relationships underlying the periodic table It suggests that the table has more dimensions than the page – based solely on the actual chemistry of the elements. If the plot is extended to a third dimension, the algorithm compromises somewhat, moving hydrogen and the alkali metals ‘up’ out of the plane to approach each other.Īll of this feels right, wouldn’t you say? The exercise both reaffirms the traditional groupings of the periodic table and reminds us of the subtle distinctions that cut across it, the individuality of certain elements. For example, a two-dimensional plot obtained using data on perovskite stability places the group 1 elements close together, but hydrogen’s ability to act as a hydride (of which the machine has no understanding) makes it slippery and puts it in the neighbourhood of halogens and chalcogens. For the most part, elements of the same group are all found together, but the exceptions show where the algorithm has divined some difference in character for an element in the context of a particular dataset. The reverse process – colouring the low-dimensional plots themselves according to the conventional element groups – has a similar effect. Elements closer together are more alike in their contribution to the energy of compounds in the dataset Hydrogen is consistently anomalous at the top of group 1.ĭata-driven arrangements of the elements according to their effect on the stability of elpasolites (left) and perovskites (right). Fluorine can look rather different from the other halogens indeed, the whole of the first p block row may display a character distinct from those below. But in some tables these boundaries are more revealing. The projections typically colour the columns much as we’d intuitively expect: for example, the noble gases appear almost identical to one another and distinct from all the others, the halogens tend to form a monochrome column, and the alkali metals too have a unity that is close in colour to that of the alkaline earth metals. Ceriotti and colleagues answer that question with an easily eyeballed, graduated colour-coding of elements in the conventional format of the table such that similarities are reflected in their hue. An obvious question is whether the proximity of elements in these projections matches the relationships found in the periodic table. In these representations, certain elements are found to cluster together in the low-dimensional space. Ceriotti’s team has used other datasets too, producing other representations of the elemental relationships. It’s a little like the old classical idea that the substances we see, such as iron or copper, are made up of the more fundamental ‘elements’ earth, air, fire and water, combined in different proportions. They then simplified these high-dimensional descriptions by mapping the elements onto a compressed, lower-dimensional space in which each element is characterised by values of a handful of abstract quantities. The exercise suggests that the periodic table has more dimensions than the page – based solely on the chemistry of the elements The structures are represented as a vector of features that describe both the geometric relations between different atoms and their chemical identity. Michele Ceriotti at the École Polytechnique Fédérale de Lausanne and his coworkers have applied ML to crystal structures drawn from a dataset 1 of around 11,000 quaternary compounds of the type ABC 2D 6, calculated for a wide range of compositions (incorporating 39 main-group elements) by density functional theory. These schemes can often identify connections invisible to humans, because we can’t generally process that much data and because the correlations may exist in high-dimensional spaces that we cannot visualise. Colouring the elements to show the ‘character’ uncovered by the algorithm reveals similarities and exceptions within the periodic table Periodic_table v2.0īoth methods use machine learning (ML): the standard form of most artificial-intelligence algorithms at present, in which relationships and correlations between variables are deduced by combing through data.
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