Abstracts Book - IMRC 2018

• SD2-P008
STUDY OF THE REACTIVITY IN Aln CLUSTERS USING ATOMIC
DESCRIPTORS AND MACHINE LEARNING METHODS
Lillian Gisela Ramirez Palma 1 , David Ignacio Ramirez Palma 1 , Cesar Garcia Jacas 1 , Fernando
Cortes Guzman 1,2
1 Instituto de Quimica UNAM, Physical Chemistry, Mexico. 2 Centro Conjunto de Investigación en
Química Sustentable UAEM-UNAM (CCIQS), Physical Chemistry, Mexico.
The Quantum Theory of Atoms in Molecules (QTAIM) allows us to partition the
space by identifying regions corresponding to an atom inside a molecule, crystal
or cluster. From this, atomic properties (such as electronic population,
delocalization indexes, multipolar moments, laplacian of electron density and
energy components) were used to describe the reactivity of Al n clusters. For this
purpose, the structures were taken The Cambridge Cluster Database and by DFT
methods the corresponding sets of orbitals were obtained to be treated under
QTAIM using Gaussian 16 and AIMAll respectively. After the extraction of atomic
properties data, an analysis based on unsupervised and supervised learning
methods was performed to determine the reactivity in the presence of carbonyl
compounds. In this way, we demonstrate the use of atomic descriptors as an
important tool for the prediction of reactivity in this type of systems.
Acknowledgment:
The authors acknowledge to CONACYT for financial support (Grants 293294 and
CB 220392). We also thank DGTIC-UNAM project LANCAD-UNAM-DGTIC-194 for
computer time and PAPIIT-UNAM project IN202717. Finally, the authors are also
thankful to Chemistry Institute, UNAM. CRGJ acknowledges the support
“Dirección General de Asuntos del Personal Académico” (DGAPA) for the
postdoctoral fellowship at “Instituto de Química, Universidad Nacional
Autónoma de México (UNAM)” in 2016-2018.
Keywords: Aluminium clusters, Machine learning, QTAIM
Presenting authors email: lila.gis.rp@gmail.com