Science
Everything about my scientific background
Interests
I am interested in the theory of the electronic structure of atoms and molecules. In the framework of my Ph.D. thesis, I was working in the characterization of metallic and insulating properties in low dimensional systems as well as other theoretical studies. I have also put online some posters and slides about the work I have done during my Ph.D. here.
Part of my work has consisted in method development. We are proposing a new indicator called Position Spread tensor that can be used to characterize chemical bonds and at the same time monitor the wave functions (Ψ).
I have developed expertise in the use of methods based on the wave function such as: Multiconfigurational selfconsistent field (CASSCF), Nelectron valence state perturbation theory (NEVPT), Multireference configuration interaction (MRCI), and Full configuration interaction.
In my first postdoctoral appointment in the Catalyst Design Lab
at Brown University, I worked on the accurate prediction of
energies, and atomic forces using machinelearning techniques.
These models are known as "interatomic machinelearning
potentials" and work in an atomcentered mode.
I also am part of the development of the Atomistic MachineLearning Package (Amp) where I worked in
general packaging aspects (I uploaded it to Debian/Ubuntu) and
implemented the Kernel Ridge Regression model, and a
neural network with charge equilibration capabilities.
In my second postdoctoral appointment in the Lawrence Berkeley
National laboratory I am exploring new machinelearning models
to solve problems in chemistry.
My postdoctoral work has made me realize I am passionate about
machinelearning applied to solve problems in sciences.
Datadriven predictions are fascinating and this field has the
advantage of being very diverse allowing one to exploit skills
in mathematics, physics, chemistry and programming!
List of published software

Github repositories
 Huckel (https://github.com/muammar/huckel)
 Centerfinder (https://github.com/muammar/centerfinder)
 Heisengerg (https://github.com/muammar/heisenberg)
This is a python program that takes your MOLPRO output file, and from the Cartesian coordinates, it forms a Huckel hamiltonian matrix to then give you the eigenvalues to be plotted against the normalized eigenvalues ordinal numbers as showed in Ref.[1], and [5]. It is also possible to delete desired Carbon atoms by indicanting their respective numbers in the structure.
This python script intends to look for localized molecular orbitals (LMO) near to atoms based on the center of charges and prepare a MOLPRO input file in order to perform later incremental calculations as stated in H. Stoll, Chem. Phys. Lett., 1992, 19. This is normally a very tedious work to be done by hand, and it’s there when centerfinder comes to help.
A python program written by E. Fertitta and myself to build a heisenberg hamiltonian matrix, diagonalize it and calculate the Position Spread tensor.

Total PositionSpread tensor computed in the MULTI module of Molpro (See: https://www.molpro.net/info/2015.1/doc/manual/node279.html).
List of publications

El Khatib M., Evangelisti S., Leininger T., Bendazzoli G.L., “A Theoretical Study of Closed Polyacene Structures”, Phys. Chem. Chem. Phys., 14, 1566615676 (2012). DOI: 10.1039/C2CP42144E

Brea O., El Khatib M., Angeli C., Bendazzoli G.L., Evangelisti S., Leininger T., “Behavior of the PositionSpread Tensor in Diatomic Systems”, J. Chem. Theory Comput., 9, 52865295 (2013). DOI: 10.1021/ct400453b

El Khatib M., Leininger T., Bendazzoli G.L., Evangelisti S.., “Computing the PositionSpread tensor in the CASSCF formalism”, Chem. Phys. Lett., 591, 5863 (2014). DOI: 10.1016/j.cplett.2013.10.080

Bendazzoli G.L., El Khatib M., Evangelisti S., Leininger T., “The Position Spread Tensor in Mixed Valence Compounds: a Study on the H + 4 Model System”, J. Comput. Chem., 35, 802808 (2014). DOI: 10.1002/jcc.23557

El Khatib M., Evangelisti S., Leininger T., Bendazzoli G.L., “Partly Saturated Polyacene Structures: a Theoretical Study”, J. Mol. Model. 20, 2284 (2014). DOI: 10.1007/s0089401422847

El Khatib M., Bendazzoli G.L., Evangelisti S., Helal W., Leininger T., Tenti L., Angeli C., “Beryllium Dimer: a Bond based on nonDynamical Correlation”, J. Phys. Chem. A, 118, 6664 (2014). DOI: 10.1021/jp503145u

El Khatib M., Brea O., Fertitta E., Bendazzoli G.L., Evangelisti S., Leininger T., “The total positionspread tensor: Spin partition”, J. Chem. Phys., 142, 094113 (2015). DOI: 10.1063/1.4913734

El Khatib M., Brea O., Fertitta E., Bendazzoli G.L., Evangelisti S., Leininger T., Paulus B., “Spin delocalization in hydrogen chains described with the spinpartitioned TotalPosition Spread tensor”, Theor. Chem. Acc., 134, 1 (2015). DOI: 10.1007/s0021401516257

E. Fertitta, M. El Khatib, G.L. Bendazzoli, S. Evangelisti, T. Leininger, B. Paulus, “The Spin Partitioned TotalPosition Spread tensor: an application to Heisenberg spin chains”, J. Chem. Phys., 143, 244308 (2015). DOI: 10.1063/1.4936585

M. El Khatib, "Characterization of metallic and insulating properties of lowdimensional systems", Ph.D. Manuscript, 2015. http://thesesups.upstlse.fr/2926/

O. Brea, M. El Khatib, C. Angeli, G.L. Bendazzoli, S. Evangelisti, T. Leininger, “The SpinPartitioned TotalPosition Spread: an application to diatomic molecules”, J. Phys. Chem. A, 120, 5230 (2016). DOI: 10.1021/acs.jpca.6b01043

A. Khorshidi, Z. Ulissi, M. El Khatib, A.A. Peterson, "Amp: The Atomistic Machinelearning Package v0.5", (2017). DOI: doi:10.5281/zenodo.322427

A. Khorshidi, M. El Khatib, A.A. Peterson, "Amp: The Atomistic Machinelearning Package v0.6", (2017). DOI: doi:10.5281/zenodo.836788
Profiles
More information soon.
Personal projects
Proyecto Ciencia
Founder and member of a project called “Proyecto Ciencia” which is conformed by a group of professionals and students who are aiming to contribute to the development of the science in Latin America.