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Muammar El Khatib

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Muammar El Khatib Rodriguez Ph.D.
Postdoctoral Scholar
Berkeley, California US

Muammar El Khatib is a postdoctoral scholar in the Computational Chemistry, Materials and Climate group at Lawrence Berkeley National Laboratory (LBL).

He is a chemist by training from the University of Zulia in Venezuela and started his graduate studies with a European Master in Theoretical Chemistry and Computational modeling of the Erasmus Mundus Program. His Ph.D. in theoretical chemical physics was about the characterization of metallic and insulating properties of low-dimensional systems using the theory of the insulating state of Walter Kohn applied with wave function theory.

Prior to LBL, he was a postdoctoral research associate at Brown University where he worked in the acceleration of atomistic simulations with machine learning models in the group of Prof. Andrew A. Peterson in the Catalyst Design Laboratory. In this appointment, he acquired experience with neural network and kernel ridge regression models to mimic quantum mechanics simulations using interatomic machine learning potentials.

He has published 10 papers, given presentations at international conferences, and developed a module for the Molpro quantum chemistry package, and the atomistic machine-learning package (Amp). Additionally, he has participated in the free software community and is a Debian Linux developer.

At LBL, he is working towards the development of machine learning approaches, algorithms and data sets to solve chemical sciences problems.

Journal Articles

Oriana Brea, Muammar El Khatib, Celestino Angeli, Gian Luigi Bendazzoli, Stefano Evangelisti, Thierry Leininger, "The Spin-Partitioned Total-Position Spread: an application to diatomic molecules", J. Phys. Chem. A, 120, 5230 (2016), March 25, 2016,

Muammar El Khatib, Oriana Brea, Edoardo Fertitta, Stefano Evangelisti, Thierry Leininger, Gian Lugi Bendazzoli, "The total position- spread tensor: spin partition", J. Chem. Phys. 142, 094113 (2015), March 5, 2015,

Muammar El Khatib, Gian Luigi Bendazzoli, Stefano Evangelisti, Wissam Helal, Thierry Leininger, Lorenzo Tenti, Celestino Angeli, "Beryllium- Dimer: a Bond based on non-Dynamical Correlation", J. Phys. Chem. A, 6664 (2014), May 27, 2014,

Presentation/Talks

Muammar El Khatib, Alireza Khorshidi, Andrew A Peterson, Acceleration of Saddle-Point Searches Assisted by Machine Learning, 68 th Annual Meeting of the International Society of Electro-chemistry, August 31, 2017,

Posters

Muammar El Khatib, Andrew A Peterson, "Local Chemical Environments In Machine Learning", Gordon Research Conference: Towards Next-Generation Challenges in Computational Chemistry: From Quan- tum Chemistry and Molecular Simulation to Data Discovery and Quantum Computing., July 21, 2018,

Others

Alireza Khorshidi, Muammar El Khatib, Andrew A Peterson, Amp: The Atomistic Machine-learning Package v0.6.1, August 1, 2018,

Alireza Khorshidi, Muammar El Khatib, Andrew A Peterson*, Amp: The Atomistic Machine-learning Package v0.6, https://bitbucket.org/andrewpeterson/amp, July 31, 2017,

Alireza Khorshidi, Zachary Ulissi, Muammar El Khatib, Andrew A Peterson, Amp: The Atomistic Machine-learning Package v0.5, https://bitbucket.org/andrewpeterson/amp, February 24, 2017,