Postdoctoral Research Associate - Computational Chemist

Requisition Id 13380 

Overview: 

As a U.S. Department of Energy (DOE) Office of Science national laboratory, ORNL has an extraordinary 80-year history of solving the nation’s biggest problems. We have a dedicated and creative staff of over 6,000 people! Our vision for diversity, equity, inclusion, and accessibility (DEIA) is to cultivate an environment and practices that foster diversity in ideas and in the people across the organization, as well as to ensure ORNL is recognized as a workplace of choice. These elements are critical for enabling the execution of ORNL’s broader mission to accelerate scientific discoveries and their translation into energy, environment, and security solutions for the nation.

 

We are seeking a Postdoctoral Research Associate who will focus on applying computational methods to perform multiscale modeling and simulation for polymers far from equilibrium.  Experience with scalable computational chemistry and/or materials simulation software (LAMMPS) from classical MM to coarse-graining on HPC platforms and machine learning (ML) capabilities is desired. This position resides in the Computational Chemistry and Nanomaterials Sciences Group of the Computational Sciences and Engineering Division (CSED), Computing and Computational Sciences Directorate (CCSD) at Oak Ridge National Laboratory (ORNL). In this role, you will perform computational studies aimed at developing atomistic models and perform simulations for the properties of polymers under non-equilibrium processes.

 

Computational methods will primarily involve density functional theory, density functional tight binding, classical molecular mechanics, coarse-graining, and machine-learning potentials to run molecular dynamics simulations. You will also work within a multidisciplinary, multi-institutional team involving specialists in theory, polymer synthesis, and small angle neutron scattering (SANS) characterization.

 

Major Duties/Responsibilities: 

• Work with a diverse team of scientists seeking to advance scientific understanding of structural and dynamic aspects of multi-functional polymers.
• Conduct molecular simulations to elucidate the equilibrium/non-equilibrium dynamics.
• Independently plan and conduct simulations and work with experimentalists to help interpret SANS results and understand structural/dynamic behaviors.
• Participate in project planning and execution.
• Present and report research results and publish scientific results in peer-reviewed journals in a timely manner.
• Ensure compliance with environment, safety, health, and quality program requirements.
• Maintain strong dedication to the implementation and perpetuation of multi-scale models for polymers.
• Deliver ORNL’s mission by aligning behaviors, priorities, and interactions with our core values of Impact, Integrity, Teamwork, Safety, and Service. Promote diversity, equity, inclusion, and accessibility by fostering a respectful workplace – in how we treat one another, work together, and measure success.  

 

Basic Qualifications:

• A PhD in theoretical or computational chemistry or closely related field in physical chemistry or chemical physics completed within the last five years
• Experience with static Density Functional Theory (DFT) calculations (e.g., Quantum Espresso, ORCA, NWChem) and Density Functional Tight Binding (DFTB) calculations (DFTB+) classical molecular dynamics (MD) simulations or MD simulations based on DFT theory (e.g., Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS)), Machine-Learning potentials development (e.g., TorchANI, DeepMD)

 

Preferred Qualifications:

• Experience or knowledge in some of the following areas: (i) molecular dynamics simulation and analysis for polymer dynamics from scratch (ii) experience in DFT and DFTB (iii) experience in enhanced sampling  (e.g., Steered molecular dynamics, Metadynamics); (iv) experience in machine learning potential training (v) transition state calculations (vi) programming skills for Linux systems
• Excellent record of productive and creative research as demonstrated by publications in peer-reviewed journals