Everything about my scientific background


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

  • Github repositories

    • Huckel (
    • 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.

    • Centerfinder (
    • 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.

    • Heisengerg (
    • 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:

List of publications

  1. 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

  2. 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

  3. 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

  4. 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

  5. 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

  6. 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

  7. 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

  8. 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

  9. 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

  10. M. El Khatib, "Characterization of metallic and insulating properties of low-dimensional systems", Ph.D. Manuscript, 2015.

  11. 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

  12. 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

  13. 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.

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.

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.