1. Hidden descriptors: Using statistical treatments to generate better descriptor sets
- Author
-
Lucía Morán-González and Feliu Maseras
- Subjects
Hidden descriptors ,Chemical descriptors ,Bond dissociation energy ,Activation energy ,Metal fragments ,Ligands ,Chemistry ,QD1-999 ,Electronic computers. Computer science ,QA75.5-76.95 - Abstract
The application of artificial intelligence to chemistry usually focuses on the identification of good correlations between descriptors and a given property of interest. The descriptors often come from arbitrary sets, with the implicit assumption that the evaluation of a sufficiently wide range of descriptors will lead to a satisfactory choice. Recent work in our group has focused on applying statistical analysis to large amounts of DFT results with the goal of finding optimal descriptor sets for a given property, which we label as hidden descriptors. This article briefly discusses this treatment and the chemical knowledge that has been gained through its application in two different domains: metal-ligand bond strength in transition metal complexes, and energy barriers in bimolecular nucleophilic substitution reactions.
- Published
- 2024
- Full Text
- View/download PDF