Professional Interests

My field of research is computational science where I mainly work with methodology and algorithm development. From my current and earlier projects I have gained insights in the use of both distributed, grid and parallel computing facilities. I have also gained strong analytic skills from data processing, especially visualization.

Experience

I am an experienced implementer and tester of simulation tools for atomistic systems using C++, Python and Matlab. The research I have conducted have mainly revolved around the adaptive kinetic Monte Carlo Method (AKMC), which is a method for long time scale simulations of the so called rare-event systems. When AKMC is applied to simulate the dynamical behavior of such systems the computationally demanding part is to locate the relevant saddle points surrounding the present state of the system. However, a fortunate property of this problem is that it easily can be handled with both parallel and distributed computers. To refine and implement the algorithms solving the task of sampling saddle points on high dimensional surfaces are among my core competences.

Other computational techniques which I have applied in my research projects are, global minimization, minimazation, molecular dynamics, temperature accelereted dynamics and parallel replica. In most of my research emperical potentials have been applied to account for the atomistic interactions, however, I have also done work where interactions are obtaining using more accurate DFT codes.

Extended Collaboration Visits

Prof. G. Henkelman Department of Chemistry and Biochemistry, UT, USA
Prof. L. Pizzagalli PHYMAT, University of Poitiers, FR
Prof. H. Jónsson Department of Chemistry, UW, USA
Prof. J. Schiøtz Department of Physics, DTU, DK
Prof. A. Voter T-12, LANL, USA