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Hidden descriptors: Using statistical treatments to generate better descriptor sets

التفاصيل البيبلوغرافية
العنوان: Hidden descriptors: Using statistical treatments to generate better descriptor sets
المؤلفون: Lucía Morán-González, Feliu Maseras
المصدر: Artificial Intelligence Chemistry, Vol 2, Iss 1, Pp 100061- (2024)
بيانات النشر: Elsevier, 2024.
سنة النشر: 2024
المجموعة: LCC:Chemistry
LCC:Electronic computers. Computer science
مصطلحات موضوعية: Hidden descriptors, Chemical descriptors, Bond dissociation energy, Activation energy, Metal fragments, Ligands, Chemistry, QD1-999, Electronic computers. Computer science, QA75.5-76.95
الوصف: 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.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2949-7477
04740548
العلاقة: http://www.sciencedirect.com/science/article/pii/S2949747724000198Test; https://doaj.org/toc/2949-7477Test
DOI: 10.1016/j.aichem.2024.100061
الوصول الحر: https://doaj.org/article/94f92b65e04740548504c9b4511df0a9Test
رقم الانضمام: edsdoj.94f92b65e04740548504c9b4511df0a9
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:29497477
04740548
DOI:10.1016/j.aichem.2024.100061