Postdoctoral Research Associate - Computational Chemistry

Overview

We are seeking a Postdoctoral Research Associate who will focus on developing and applying machine learning interatomic potentials (MLIPs) and free energy methods to ion separations, interfacial science, and energy-related processes. This position resides in the Chemical Separations Group in the Separations and Polymer Section, Chemical Sciences Division, Physical Sciences Directorate, at Oak Ridge National Laboratory (ORNL). In this role, you will develop and/or fine-tune MLIPs using ab initio calculations and run molecular dynamics simulations to understand the thermodynamics of ion complexation in the bulk and transfer across liquid–liquid interfaces. You will also work within a multidisciplinary multi-institutional team involving specialists in theory, spectroscopy, organic synthesis, and separations.

Major Duties/Responsibilities

• Work with a diverse team of scientists seeking to advance scientific understanding of structural and energetic aspects of ions binding and separation
• Develop, fine-tune, validate, and deploy MLIPs, including MACE or related architectures and foundation models, for ions and ligands in single and two-phase separation systems
• Conduct molecular dynamics with MLIPs using LAMMPS, OpenMM, or comparable simulation software to elucidate the thermodynamic and structural basis of enhanced binding and selectivity manifested in improved separations
• Calculate ion solvation, ligand binding, and complexation free energies using thermodynamic integration and related methods; quantify uncertainty and assess convergence
• Investigate ion transport and proton transfer at liquid interfaces; elucidate how interfacial structure and solvent organization affect reaction and transfer free energies
• Independently plan and conduct simulations and work with experimentalists to help interpret experimental results and guide the design of new chelating agents
• Present and report research results and publish scientific results in peer-reviewed journals in a timely manner

Basic Qualifications

• A PhD in theoretical or computational chemistry or closely related field in physical chemistry or chemical physics completed within the last 5 years
• Experience with density functional theory (DFT) (e.g., VASP, Gaussian, Orca) and/or molecular dynamics (MD) simulations using ab initio and machine learning potentials (e.g., LAMMPS, OpenMM, CP2K, Psi4)
• Experience in employing advanced statistical mechanics methods for simulations of materials (e.g., thermodynamic integration)

Preferred Qualifications

• Experience with Message Passing Atomic Cluster Expansion (MACE) or related equivariant MLIPs, foundation models, transfer learning, fine-tuning, or active learning
• Background in molecular simulations of ions in liquids, including the treatment of long-range electrostatics and polarization
• Demonstrated ability to compute free energies by thermodynamic integration, free energy perturbation, MBAR, umbrella sampling, or related approaches
• Proficiency in applying electronic structure methods and running parallel calculations on high-performance computing systems
• Familiarity with electronic structure methods for excited states
• Excellent record of productive and creative research as demonstrated by publications in peer-reviewed journals
• Excellent written and oral communication skills
• Motivated self-starter with the ability to work independently and to participate creatively in collaborative teams across the laboratory
• Ability to function well in a fast-paced research environment, set priorities to accomplish multiple tasks within deadlines, and adapt to ever changing needs

Special Requirements

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.