Postdoctoral Research Associate - Computational Chemistry and Nanomaterials Sciences
Organisation Name: Oak Ridge National Laboratory
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Overview: The Computational Sciences and Engineering Division (CSED) is seeking a Postdoctoral Research Associate who will focus on the application and development of advanced atomic-scale simulations of complex systems through approximate quantum chemical methods in combination with machine learning corrections and cheminformatics. The position is funded in equal parts by the U.S. Department of Energy (DOE), Basic Energy Sciences (BES) and Fossil Energy (FE) program, and the goal is to develop methodologies that will enable a) transformative advances in understanding and controlling heterogeneous catalysis (BES funded), and b) identify chemical routes for the breakdown of coals and pitches to upcycle them into carbon nanotubes and nanofibers as value-added products (FE funded). 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). As part of our research team, you will be involved in the application and development of the density-functional tight-binding (DFTB) method within the frameworks of the DFTB+ and LAMMPS codes on parallel computing platforms. Classical molecular mechanics and neural network potential (PyTorch- or TensorFlow-based) will be incorporated to describe key behaviors and mechanisms of targeted systems. The multiscale approach with various methods, including DFT/DFTB/empirical reactive force field/neural networks, will be applied to capture complex chemical transformation processes at scales. The goal is to develop methodologies that will a) enable transformative advances in understanding and controlling heterogeneous catalysis (50% effort), and b) identify chemical routes for the breakdown of coals and pitches to upcycle them into carbon fibers and major components as value-added products (50% effort). Our studies bridge the system size and simulation time gap between first principles Born-Oppenheimer molecular dynamics simulations and large-scale simulations. The projects offer an opportunity to closely interact and collaborate with large experimental teams and engage in theory-experiment feedback loops. The insights gained from such combined multiscale simulations/experimental work will drive the development of novel heterogeneous catalysis as well as energy-efficient engineering process from coal and pitches to various value-added products such as carbon fiber, revolutionizing energy-intensive chemical processes of the future. Major Duties/Responsibilities: Perform simulations to predict complex processes such as surface reconstructions of perovskites, metal-support interactions and dynamic processes in nanometer-scale, long-timescale molecular dynamics simulations Perform simulations and develop a theoretical framework to derive possible chemical molecules from coals based on machine learning (ML) techniques (e.g., generative adversarial networks – GAN – and/or graph neural network – GNN) and cheminformatics Scale up currently existing neural network correction tools based on TensorFlow and create efficient interfaces for on-the-fly corrected DFTB/MD simulations Develop neural-network potentials for larger-scale simulations of coal-related molecules Develop and apply these methodologies in close collaboration with the experimental and theoretical groups of the BES Nanocatalysis and FE Coal-to-Products FWP projects 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/Computational Chemistry, Molecular/Solid State Physics, or a related discipline completed within the last five years Programming experience in atomistic simulation codes (C++, Python and/or Fortran90/95/2008) for parallel computing with MPI/OpenMP/CUDA Research experience in applying machine learning algorithms to molecular or atomistic systems for their properties, structures, and designs Preferred Qualifications: Experience with Born-Oppenheimer molecular dynamics simulations and/or free energy perturbation and other methods of quantifying thermodynamics in silico Research experience in applying and modifying Python-based cheminformatics packages such as RDKit or ASE 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 Additional Information: Applicants cannot have received their Ph.D. more than five years prior to the date of application and must complete all degree requirements before starting their appointment. 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. This position will remain open for a minimum of 5 days after which it will close when a qualified candidate is identified and/or hired. We accept Word (.doc, .docx), Adobe (unsecured .pdf), Rich Text Format (.rtf), and HTML (.htm, .html) up to 5MB in size. Resumes from third party vendors will not be accepted; these resumes will be deleted and the candidates submitted will not be considered for employment. If you have trouble applying for a position, please email ORNLRecruiting@ornl.gov. 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.
