Abstracts Book - IMRC 2018

• SD2-O006
FILLING THE GAPS IN A BENCHMARK OF WEAKLY INTERACTING
CALIXARENE-GUESTS COMPLEXES THROUGH MACHINE
LEARNING PREDICTION METHODS.
Joaquin Barroso Flores 1,2
1 Universidad Nacional Autónoma de México, Centro Conjunto de Investigación en Química
Sustentable, Mexico. 2 Universidad Nacional Autónoma de México, Instituto de Química,
Mexico.
Supramolecular arrangements held together by weak non-covalent interactions
have broad applications and consequences from biochemistry to drug delivery
and selective reversible sequestration of molecules. There are many available
quantum mechanical methods to calculate the interaction energy between
fragments but so far there is no one-size-fits-all kind of method, e.g. the use of
population analysis based methods -from NBODel to QTAIM- rely heavily on the
partition method of the electron density whether this is made over atomic
orbitals or basins defined by the laplacian of the electron density. On the other
hand, the so called supramolecular method relies on the calculation of
deformation energies from and to somewhat arbitrary molecular conformations
along a possibly complex potential energy hypersurface, and thus it results
heavily heuristical. Our group has produced hundreds of calculations regarding
the interaction energies between various calix[n]arenes (aromatic macrocycles
which can bear almost any functional group imaginable) and several organic and
inorganic molecules that are hosted in their cavities for various purposes.
However, through the use of machine learning methods this information can be
used to generate a benchmark for these macruocycles to predict the correct
interaction energies, which is crucial in the design of calixarene based functional
materials. Also, a trend which points to the best quantum method for calculating
interaction energies could be developed in this way.
Acknowledgment:
DGTIC - UNAM for granting access to their supercomputing mainframe known
as Miztli.
Keywords: Host-Guest, Calixarenes, Interaction Energies
Presenting authors email: jbarroso@unam.mx