Machine learning: Designing Chiral Crystals

Logistic regression is a statistical method that can tell apart two objects and is also compared it to use in smartphones. In face recognition, smartphones use machine learning to classify faces and things that are not faces, so we can train our model to detect chiral and non-chiral possibilities, but here the input is not an image, its information."

Chirality describes the quality of possessing a mirror image to something else, but without the ability to superimpose it. Your left foot, for example, is a mirror of your right. They look similar, but they are not the same.

In chemistry two molecules can have the same makeover of elements, but still they differ in their geometry, also a left-handed chiral helix can have a corresponding to the right-handed helix.

But, making a mirror image of a chiral molecule is more difficult than just simply rearranging some bonds and is more complex when making a crystal, in this case a highly arranged series of atoms or molecules exist in three dimensions.

The scientists analysed about the crystals that there are 686 chiral crystals and 1000 achiral crystals from the most recent Inorganic Crystal Structure Database by using the technique logistic regression. Logistic regression analysis model predicts ideal chiral crystal also used to predict which chemical groups are best for making chiral molecules and also used to calculate which chemical groups of the periodic table have elements or atoms that are more likely to coexist in a chiral crystal and the groups that correspond to carbon, nitrogen, and oxygen are the best with group numbers 14, 15, and 16 respectively.

The most interesting and difficult part of making a chiral crystal are to know how to design them; it is difficult to mix different atoms so that their chiral geometry coexists in a single crystal. From this chiral crystal we can predict crystals and explore how to build a chiral magnet from them.

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Jessica Mark

Program Manager | Crystallography Congress 2018

Email: novelmaterials@materialsconferences.org