How can Databases assist with the Prediction of Chemical Compounds?

Abstract

An overview is given on the ways databases can be employed to aid in the prediction of chemical compounds, in particular inorganic crystalline compounds. Methods currently employed and possible future approaches are discussed.

Accelerated materials property predictions and design using motif-based fingerprints

“…[1][2][3][4][5][6][7][8][9][10][11][12][13][14][15] This emerging trend is fueled by the availability and emergence of large materials databases, [16][17][18] as well as our ability to progressively accumulate materials data via high-throughput computations 19,20 and experiments. [16][17][18] Data-driven strategies aimed at rapid property predictions, and ultimately at rational or informed materials design, rely on exploiting the information content of past data, and using such information within heuristic or statistical interpolative learning models to provide estimates of properties of a new material.…”

Synthesis Target Structures for Alkaline Earth Oxide Clusters

“…Configuration 8-XBBB has one less bond in each of the two 2 × 4 layers, and represents structures composed of two parallel atomic layers, still only one bond length apart, that do not only contain tetragons (in this example, each of the two sequences of three tetragonal faces become one tetragonal and one hexagonal face). Configuration 8-AXXC has a further bond reduction between parallel layers; the original 8-BAAA configuration has four sides of three tetragonal faces, each becoming one tetragonal and one hexagonal face, and in doing so creating a larger void in the centre of the cluster (than the combined volume within the (BaO) 4 cuboid and (BaO) 6 drum, as in configuration 8-XBBB). Only for (MgO) n , the compound with the smallest cation, is the bubble structure the GM and the intermediate 8-XBBB configuration found for (BaO) 8 is unstable for MgO, indicating that perhaps there are more stable LM overall for larger-cation clusters.…”

“…The absolute deviations of the angles are shown in Figure 4. For reference as to how this looks in specific clusters, see the n = 4 structures in Table 1, where the (MgO) 4 and (BaO) 4 structures are deformed in opposite ways. In the case of magnesium oxide, the Mg-O-Mg angles were more acute than the expected angle (and correspondingly O-Mg-O angles were wider).…”

“…This should allow for a very efficient way of finding minima, and ideally the global minima (GM), for magnesium oxide, calcium oxide and strontium oxide clusters of the same sizes. Similar data-mining techniques have previously been employed for both bulk crystal structures [4,5] and by us for other inorganic nanoclusters [6]. For a more general review of how databases can assist with the prediction of chemical compounds, we refer the reader to Ref.…”

The Flexible Unit Structure Engine (FUSE) for probe structure-based composition prediction

“…4,5 There have been notable successes from such approaches 6,7 to identifying missing elemental compositions or new high-pressure phases of materials. Much effort in inorganic materials chemistry has focused on the discovery of new materials with complex compositions or on the prediction of new compositions that can adopt previously known crystal structures -in either case, the problem becomes very complex once we consider systems containing more than three elements due to the number of possible phase elds and the size of the compositional phase space.…”