Quantum Simulation and Data Science Lab

Quantum Simulation and Data Science Lab

Major Competencies

The Quantum Simulation and Data Science Lab have experience in developing and using ab initio based methods and scientific software codes for quantum materials modelling and atomistic simulations in response to external stimuli such as light, and electric/magnetic fields. Simulations provide valuable new insights into the chemical and physical process and help us to discover new materials for renewable and sustainable energy. The lab seeks to advance quantum materials modelling and atomistic simulations by exploiting emerging technologies in high-performance computing (HPC), data science (artificial intelligence and machine learning), and quantum computers.







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Head of the lab

Dr. Hemanadhan Myneni

Research Specialist of Faculty of Industrial Engineering, Mechanical Engineering and Computer Science at the University of Iceland

Hemanadhan is a seasoned theoretical physicist and computer scientist. His core expertise revolves around the development of novel theoretical methods and the creation of scientific software codes tailored for electronic structure modelling. This specialization finds applications in various fields, including quantum chemistry, condensed matter physics, and materials science. Currently, Hemanadhan is immersed in crafting computational software designed for the simulation of chemical systems.

Advisory Board Member

Dr. -Ing. Andreas Lintermann

Leader Simulation and Data Lab "Highly Scalable Fluids & Solids Engineering" and Coordinator of the European Center of Excellence in Exascale Computing CoE RAISE

Dr.-Ing. Andreas Lintermann is a postdoctoral researcher at Forschungszentrum Jülich, a member of the Helmholtz Association, Germany. At the Jülich Supercomputing Centre (JSC), he is heading the Simulation and Data Laboratory ‘‘Highly Scalable Fluids & Solids Engineering’’, which is also part of the Jülich Aachen Research Alliance Center for Simulation and Data Science (JARA-CSD). His research focuses, amongst others, on lattice-Boltzmann methods, artificial intelligence, high-performance computing, heterogeneous computing on modular supercomputing architectures, high-scaling meshing methods, efficient multi-physics coupling strategies, and bio-fluidmechanical analyses of respiratory diseases. He is the coordinator of the European Center of Excellence in Exascale Computing “Research on AI- and Simulation-Based Engineering at Exascale” (CoE RAISE).

Projects & Cooperations

All IHPC Projects
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Selected Publications

H. Myneni, D. Kedziera, J.W. Andzelm, J. Austad, E.I. Tellgren, T. Helgaker, and K. Szalewicz, Effects of strong magnetic fields on water from rigorous quantum calculations (Manuscript: https://www.dropbox.com/s/it6r1cbx8b1k3et/Hemanadhan_Krzysztof_Water1.pdf?dl=0)

H. Myneni, E.O. Jónsson, H. Jónsson*, and A.O. Dohn*, Polarizable force field for acetonitrile based on the single-center multipole expansion, The Journal of Physical Chemistry B 126, 9339 (2022); https://doi.org/10.1021/acs.jpcb.2c04255

K. Trepte, S. Schwalbe, S. Liebing, W.T. Schulze, J. Kortus, H. Myneni, A.V. Ivanov, and S. Lehtola, Chemical bonding theories as guides for self-interaction corrected solutions: multiple local minima and symmetry breaking, The Journal of Chemical Physics 155, 224109 (2021); https://doi.org/10.1063/5.0071796

S. Jana, H. Myneni, S. Śmiga, L.A. Constantin, and P. Samal, Benchmark test of a dispersion corrected revised Tao–Mo semilocal functional for thermochemistry, kinetics, and noncovalent interactions of molecules and solids, The Journal of Chemical Physics 155, 114102 (2021); https://doi.org/10.1063/5.0060538

A. Patra, S. Jana, H. Myneni, and P. Samal, Laplacian free and asymptotic corrected semilocal exchange potential applied to the band gap of solids, Physical Chemistry Chemical Physics 21, 19639 (2019); https://doi.org/10.1039/C9CP03356D

C. Shahi, P. Bhattarai, K. Wagle, B. Santra, S. Schwalbe, T. Hahn, J. Kortus, K.A. Jackson, J.E. Peralta, K. Trepte, S. Lehtola, N.K. Nepal, H. Myneni, B. Neupane, S. Adhikari, A. Ruzsinszky, Y. Yamamoto, T. Baruah, R.R. Zope, and J.P. Perdew, Stretched or noded orbital densities and self-interaction correction in density functional theory, The Journal of Chemical Physics 150, 174102 (2019); https://doi.org/10.1063/1.5087065

S. Jana, A. Patra, L.A. Constantin, H. Myneni, and P. Samal, Long-range screened hybrid functional theory satisfying the local density linear response, Physical Review A 99, 042515 (2019); https://doi.org/10.1103/PhysRevA.99.042515

S. Jana, B. Patra, H. Myneni, and P. Samal, On the many-electron self-interaction error of the semilocal exchange hole based meta-GGA level range-separated hybrid with the B88 hybrids, Chemical Physics Letters 713, 1 (2018); https://doi.org/10.1016/j.cplett.2018.10.007

H. Myneni and M.E. Casida, On the calculation of ∆<Ŝ^2>_i for electronic excitations in time-dependent density-functional theory, Computer Physics Communications 213, 72 (2017); http://dx.doi.org/10.1016/j.cpc.2016.12.011

P. Singh, M.K. Harbola, M. Hemanadhan (H. Myneni), A. Mookerjee, and D.D. Johnson, Better band gaps with asymptotically corrected local exchange potentials, Physical Review B 93, 085204 (2016); http://dx.doi.org/10.1103/PhysRevB.93.085204