NCSI: Parallel Numerical Simulation of Energy Transport and Conversion in 2D Nano Materials

Status	Completed
Mentor Name	Zlatan Aksamija
Mentor's XSEDE Affiliation	Startup and Education Allocations, currently applying for a Research Allocation
Mentor Has Been in XSEDE Community	4-5 years
Project Title	Parallel Numerical Simulation of Energy Transport and Conversion in 2D Nano Materials
Summary	This undergraduate student experience will involve developing parallel numerical codes for the simulation of energy transport and conversion in nanostructured semiconductor materials and devices. We will then proceed to study nanostructured thermoelectric devices for energy conversion with the goal of improving energy conversion efficiency and energy performance in nanoelectronics.
Job Description	The apprentice will continue to be a part of a tight-knit research group with diverse research interests relating to numerical device simulation and ranging from thermoelectric energy conversion, quantum transport, electronic structure, 2-dimensional (2D) materials, silicon MOSFETs, and dissipation in nanoelectronics. The apprenticeship in the Fall of 2019 will focus specifically on nanostructured 2-dimensional materials (graphene, hBN, MoS2) as platforms to achieve high thermoelectric energy conversion efficiency for waste heat recovery applications. The work will be based on further developing our in-house Monte Carlo code for the solution of the Boltzmann transport equation, paralellizing it to run on a cluster, and coupling it with first principles methods for the modeling of electronic and vibrational spectra of nanostructures. The key component of the apprenticeship will be further developing and utilizing a parallel version of the Monte Carlo code for the simulation of electronic and thermal transport in nanostructured semiconductor devices, which was adapted from a previous version for 3D materials to 2D materials, specifically graphene, in the summer of 2019. This work is ongoing and almost complete, while the work on utilizing the first principles code Quantum Espresso to model electronic and vibrational structure is beginning. In the Fall, we will proceed to use this method to study and optimize nanocomposite thermoelectric devices for energy conversion with the goal of optimizing energy conversion efficiency and performance through material composition and structure. The apprentice will develop a simple but powerful and scalable numerical simulation code utilizing a combination of either C/MPI or Matlab/CUDA platforms. The apprentice will test the code on the HPC cluster at the Massachusetts Green High Performance Computing Center (MGHPCC http://www.mghpcc.org/) or a GPU-enabled workstation in the Nanoelectronics Theory and Simulation Group at the University of Massachusetts-Amherst. The final code will be equipped with a RAPPTURE user interface and deployed on the nanoHUB.org on-line simulation and education resource, thereby making the final product accessable and useful to the broader nanoscience and simulation community. The finished tool will be used in undergraduate lectures as a parallel computing learning resource. This position will require some background in solid-state electronics, as well as some programming skills in either Python, C/C++, Fortran, or Matlab. The apprentice will gain valuable skills in both parallel/high-performance computing and numerical simulation in the context of semiconductor device engineering and applied nanoscience. Work done during this apprenticeship will have a strong impact both on the environment (through developing more energy efficient nanoelectronics) and on the broader scientific computing community (by sharing both the codes and the results of the work on-line and through publications).
Computational Resources	The student will have access to XSEDE to run simulations, specifically the Density Functional Theory code Quantum Espresso, through my Educational Allocation. I am in the process of applying for a Research Allocation to follow on from the earlier Startup Allocation, which will give us additional access to XSEDE
Contribution to Community
Position Type	Apprentice
Training Plan	The student (Peter Pawelski) has been working with me all summer as an apprentice and has made tremendous progress on the project. He has been learning in parallel to develop the Monte Carlo transport code, run Quantum Espresso, and utilize our MGHPCC cluster.
Student Prerequisites/Conditions/Qualifications
Duration	Semester
Start Date	09/01/2019
End Date	12/10/2019