Lawrence Livermore National Laboratory

Gautham Dharuman

Postdoctoral Researcher
Materials Science Division

 +1 925-422-7522

Ph.D. Dual: Computational Science and Electrical Engineering
– Michigan State University


Gautham Dharuman joined LLNL as a Postdoctoral Research Staff Member in the Computational Materials Science Group in June 2018. His research interests are broadly in molecular dynamics simulations at scale, multiscale modeling, high performance computing and machine learning. In particular, he focusses on methods and simulations for correlated charged particle systems such as those found in non-ideal plasmas and in biomolecular systems.

At LLNL, he is part of the pilot project on RAS protein that aims to understand RAS-driven cancer initiation and growth through multiscale simulations aided by machine learning to enable predictions at unprecedented length- and time-scales. His efforts include physics guided unsupervised learning, consistency studies of simulations spread across scales, and high-performant code development.

Prior to joining LLNL, he received his Ph.D. (dual) in Computational Science and Electrical Engineering from Michigan State University in May 2018, advised by Professor Michael S. Murillo.

Selected Publications

  1. G. Dharuman, L. G. Stanton, and M. S. Murillo, “Controllable non-ideal plasmas from photoionized compressed gases,” New Journal of Physics 20 (2018), 103010.
  2. G. Dharuman, L. G. Stanton, J. N. Glosli, and M. S. Murillo, “A generalized Ewald decomposition for screened Coulomb interactions,” Journal of Chemical Physics 146 (2017), 024112.
  3. G. Dharuman, J. Verboncoeur, A. Christlieb, and M. S. Murillo, “Atomic bound state and scattering properties of effective momentum-dependent potentials,” Physical Review E 94 (2016), 043205.
  4. Z. Fan, X. Tao, G. Dharuman, X. Li and L. Dong, “Modeling and simulation of an ultrasensitive electron tunneling position/force nanosensor,” RSC Advances 6 (2016), 8297.
  5. X. Tao, Z. Fan, B. J. Nelson, G. Dharuman, W. Zhang, L. Dong and X. Li, “Internal electron tunneling enabled ultrasensitive position/force peapod sensors,” Nano Letters 15 (2015), 7281.

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