Postdoctoral Researcher: Machine learning to enable molecular and atomistic simulation of complex energy materials

U.S. National Renewable Energy Laboratory (NREL)

Golden, CO

The Complex Systems and Optimization Group in NREL’s Computational Science Center has an opening for a full-time postdoctoral researcher for machine learning to enable molecular and atomistic simulations of complex energy materials. In this position, the candidate will produce software and perform simulations that combine quantum mechanical  calculations of electronic structure with classical and ab initio molecular dynamics and machine learning algorithms towards the overarching goal of developing transferable and chemically aware methods for simulating complex multi-scale systems on leadership class supercomputing resources. This research will develop code to automatically create and refine classical simulations using chemical information from large-scale DFT calculations. The initial applications will be in the areas of hybrid perovskite materials and covalent organic frameworks for solar energy conversion, catalysis and separation applications. As a member of the Computational Science Center, the postdoctoral researcher also will work with NREL’s supercomputing resources, with the opportunity to work on making software and methods useable in a high-performance computing context.

Responsibilities include:

^{Collaborate with NREL and University researchers to design and implement machine learning algorithms and computational experiments to transferable  chemically aware methods for renewable energy applications, including the implementation and execution of workflows on HPC systems.}

^{Develop and implement a software package for performing automatic method parameterization based on modern machine learning algorithms and techniques, e.g. symbolic regression, compressed sensing, etc.}

^{Perform classical and quantum mechanical simulations using established codes, such as LAMMPS, Quantum Espresso, VASP, or other multi-scale or atomistic simulation codes.}

^{Evaluate and communicate results through written research reports for publication and presentations at seminars, participate in group meetings and seminars, and assist with developing grant proposals for new research directions.} 

Required Education, Experience, and Skills

Must be a recent PhD graduate within the last three years.

Preferred Qualifications

Ph.D. degree in physics, materials science, chemistry or a related field. Previous experience in one or more of the following application areas is desirable: quantum chemistry; reactive force fields; machine learning; molecular dynamics; ab initio molecular dynamics. Demonstrated knowledge of molecular or atomistic simulation methods, such as classical molecular dynamics or quantum mechanical calculations of electronic structure is desirable. Demonstrated experience with, and interest in, developing software or scientific codes and scripts to solve complex materials, chemistry, or physics problems. Creativity and competency in the planning and execution of innovative and impactful energy relevant research using advanced physical and mathematical concepts. Good verbal and written communication skills in English, and a commitment to high-quality style and language in preparation of publications and presentations. The capability to plan and manage work for multiple tasks to achieve the respective milestones in due time. A high level of self-motivation, with both the ability to work independently and the willingness to engage in collaborative research activities with industry and academic partners.

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https://nrel.wd5.myworkdayjobs.com/NREL/job/Golden-CO/Postdoctoral-Researcher--Machine-learning-to-enable-molecular-and-atomistic-simulation-of-complex-energy-materials_R3559