PURPOSE: 

Under the supervision of Dr. Stephan Irle, the successful candidate will conduct advanced atomic-scale simulations of complex systems through approximate quantum chemical methods in combination with machine learning corrections on exascale computing platforms.  The Fluid Interface Reactions, Structures and Transport (FIRST) Energy Frontier Research Center is funded by the U.S. Department of Energy (DOE), Basic Energy Sciences (BES) program.  This position resides in the Computational Chemicals and Materials Group in the Computational Sciences and Engineering Division (CSED), Computing and Computational Sciences Directorate (CCSD) at Oak Ridge National Laboratory (ORNL).   

OVERVIEW:  

The goal of the FIRST Center is to achieve a fundamental understanding and validated, predictive models of the atomistic origins of electrolyte and coupled electron transport under nanoconfinement that will enable transformative advances in capacitive electrical energy storage and other energy-relevant interfacial systems.  As part of our research team, you will be involved in the development and application of near-linear scaling implementations of the density-functional tight-binding (DFTB) method on hybrid CPU/GPU architectures such as Summit within the frameworks of the DFTB+ and GAMESS-US codes.  Bias-correction will be performed via neural networks trained at high-level data.  The methodology will be applied to the study of ion diffusion in aqueous media under confinement and redox reactions on supercapacitor electrode surfaces.  Our studies bridge the system size and simulation time gap between first principles Born-Oppenheimer molecular dynamics simulations and large-scale reactive force field simulations.  The project offers an opportunity to enhance the accuracy of DFTB potentials on the one hand, and the accuracy of reactive force fields on the other.  The insights gained from such multiscale simulations will guide experimental efforts in the development of supercapacitor systems capable of efficient ion and electron transport to achieve simultaneous high pseudocapacitive power and energy density, revolutionizing the energy economy landscape of the future.

MAJOR DUTIES/RESPONSIBILITIES: 

• Implement efficient parallel eigensolver algorithms on massively parallel supercomputer systems via GPU/CPU hybrid programming models based on MPI and MAGMA libraries as well as ELSI/ELPA.
• Perform simulations of ion diffusion/electron transport and redox reactions in aqueous solutions under confinement in supercapacitor electrodes
• Scale up currently existing neural network correction tools based on TensorFlow and create efficient interfaces for on-the-fly corrected DFTB/MD simulations
• Develop and apply these methodologies in close collaboration with the experimental and theoretical groups at the Center.
• Take advantage of leadership class high performance computing facilities available at ORNL and NERSC.
• Conduct research and report results in open literature journals, technical reports, and at relevant conferences.

BASIC QUALIFICATIONS:

• A PhD in Theoretical Chemistry, Molecular/Solid State Physics, or a related discipline completed within the last five years
• Programming and experience in major quantum chemistry codes and parallel programming techniques
• Demonstrated experience in first-principles simulations of systems under consideration of periodic boundary conditions

PREFFERED QUALIFICATIONS:

• Experience with Born-Oppenheimer molecular dynamics simulations and/or free energy free energy perturbation and other methods of quantifying thermodynamics in silico
• An excellent record of productive and creative research as demonstrated by publications in peer-reviewed journals
• Excellent written and oral communication skills and the ability to communicate in English to a scientific audience
• Motivated self-starter with the ability to work independently and to participate creatively in collaborative and frequently interacting teams of researchers  
• Ability to function well in a fast-paced research environment, set priorities to accomplish multiple tasks within deadlines, and adapt to ever changing needs

Other Information:

The appointment length will be up to 24 months with the potential for extension. Initial appointments and extensions are subject to performance and availability of funding.

Please provide a list of publications and a complete formal transcript of Ph.D. coursework when applying for this position. Three letters of reference are required and can be uploaded to your profile or emailed directly to [email protected].  Please include the title of the position in the subject line.

Apply Here: https://jobs.ornl.gov/s/RmRiHw

ORNL is an equal opportunity employer. All qualified applicants, including individuals with disabilities and protected veterans, are encouraged to apply.  UT-Battelle is an E-Verify employer.