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: Multi-configurational self-consistent field (CASSCF), N-electron 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 machine-learning techniques.
These models are known as "interatomic machine-learning
potentials" and work in an atom-centered mode.
I also am part of the development of the Atomistic Machine-Learning 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 machine-learning models to solve problems in chemistry.
My postdoctoral work has made me realize I am passionate about machine-learning applied to solve problems in sciences. Data-driven 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
- 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., and . 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 Position-Spread 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, 15666-15676 (2012). DOI: 10.1039/C2CP42144E
Brea O., El Khatib M., Angeli C., Bendazzoli G.L., Evangelisti S., Leininger T., “Behavior of the Position-Spread Tensor in Diatomic Systems”, J. Chem. Theory Comput., 9, 5286-5295 (2013). DOI: 10.1021/ct400453b
El Khatib M., Leininger T., Bendazzoli G.L., Evangelisti S.., “Computing the Position-Spread tensor in the CAS-SCF formalism”, Chem. Phys. Lett., 591, 58-63 (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, 802-808 (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/s00894-014-2284-7
El Khatib M., Bendazzoli G.L., Evangelisti S., Helal W., Leininger T., Tenti L., Angeli C., “Beryllium- Dimer: a Bond based on non-Dynamical 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 position-spread 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 spin-partitioned Total-Position Spread tensor”, Theor. Chem. Acc., 134, 1 (2015). DOI: 10.1007/s00214-015-1625-7
E. Fertitta, M. El Khatib, G.L. Bendazzoli, S. Evangelisti, T. Leininger, B. Paulus, “The Spin- Partitioned Total-Position 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 low-dimensional systems", Ph.D. Manuscript, 2015. http://thesesups.ups-tlse.fr/2926/
O. Brea, M. El Khatib, C. Angeli, G.L. Bendazzoli, S. Evangelisti, T. Leininger, “The Spin-Partitioned Total-Position 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 Machine-learning Package v0.5", (2017). DOI: doi:10.5281/zenodo.322427
A. Khorshidi, M. El Khatib, A.A. Peterson, "Amp: The Atomistic Machine-learning Package v0.6", (2017). DOI: doi:10.5281/zenodo.836788
More information soon.
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.
Posters, and other chemistry files
In this section you will find a number of different posters and slides that I have used to communicate my work in conference and meetings.