Muammar El Khatib
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 the ML4Chem machine learning package, 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.