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Feature vector clustering molecular pairs in computer simulations
KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.). Stockholm Univ, Arrhenius Lab, Dept Mat & Environm Chem, SE-10691 Stockholm, Sweden. (System and Component Design)
Stockholm Univ, Arrhenius Lab, Dept Mat & Environm Chem, SE-10691 Stockholm, Sweden.;Nanjing Tech Univ, State Key Lab Mat Oriented & Chem Engn, Nanjing 210009, Jiangsu, Peoples R China.;Petru Poni Inst Macromol Chem, Ctr Adv Res Bionanoconjugates & Biopolymers, Aleea Grigore Ghica Voda 41A, Iasi 700487, Romania..
2019 (English)In: Journal of Computational Chemistry, ISSN 0192-8651, E-ISSN 1096-987XArticle in journal (Refereed) Epub ahead of print
Abstract [en]

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-6,P-6,P-6,P-14][BOB])). The molecular pairs are successfully categorized into physically meaningful clusters, and their effectiveness is evaluated by computing the product moment correlation coefficient (PMCC). (Willett, Winterman, and Bawden, J. Chem. Inf. Comput. Sci. 1986, 26, 109-118; Downs, Willett, and Fisanick, J. Chem. Inf. Comput. Sci. 1994, 34, 1094-1102) It is observed that representative configurations of two clusters are related to two energy local minimum structures optimized by density functional theory (DFT) calculation, respectively. Several widely used clustering analysis techniques of both nonhierarchical (k-means) and hierarchical clustering algorithms are also evaluated and compared with each other. The proposed KFV technique efficiently reveals local molecular pair structures in the simulated complex liquid. It is a method, which is highly useful for liquids and solutions in particular with strong intermolecular interactions. 2

Place, publisher, year, edition, pages
Wiley , 2019.
Keywords [en]
data mining, ionic liquid, molecular structure
National Category
Theoretical Chemistry
Identifiers
URN: urn:nbn:se:kth:diva-255557DOI: 10.1002/jcc.26028ISI: 000476085400001PubMedID: 31313339Scopus ID: 2-s2.0-85069831312OAI: oai:DiVA.org:kth-255557DiVA, id: diva2:1341336
Note

QC 20190808

Available from: 2019-08-08 Created: 2019-08-08 Last updated: 2019-10-04Bibliographically approved

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