Feature vector clustering molecular pairs in computer simulations
| Autor: | Aatto Laaksonen, Han-Wen Pei |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Structural organization
010304 chemical physics Computer science business.industry Feature vector Pattern recognition General Chemistry 010402 general chemistry 01 natural sciences 0104 chemical sciences Computational Mathematics 0103 physical sciences Theoretical chemistry Molecule Artificial intelligence Cluster analysis business |
| Zdroj: | Journal of computational chemistry. 40(29) |
| ISSN: | 1096-987X |
| Popis: | A clustering framework is introduced to analyze the microscopic structural organization of molecular pairs in liquids and solutions. A molecular pair is represented by a representative vector (RV). To obtain RV, intermolecular atom distances in the pair are extracted from simulation trajectory as components of the key feature vector (KFV). A specific scheme is then suggested to transform KFV to RV by removing the influence of permutational molecular symmetry on the KFV as the predicted clusters should be independent of possible permutations of identical atoms in the pair. After RVs of pairs are obtained, a clustering analysis technique is finally used to classify all the RVs of molecular pairs into the clusters. The framework is applied to analyze trajectory from molecular dynamics simulations of an ionic liquid (trihexyltetradecylphosphonium bis(oxalato)borate ([P |
| Databáze: | OpenAIRE |
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