Postdoctoral Appointee — Computational Materials Science for Hydrogen-Metal Int

Sandia National Laboratories is the nation’s premier science and engineering lab for national security and technology innovation, with teams of specialists focused on cutting-edge work in a broad array of areas. Some of the main reasons we love our jobs:

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We are seeking a highly motivated Postdoctoral Appointee to join a multidisciplinary research team focused on computational discovery and design of materials with tunable hydrogen-metal interactions. The successful candidate will develop and apply advanced computational methods to understand, predict, and optimize metal alloys and intermetallics for applications in hydrogen storage, purification, and separations. Research topics may include hydrogen absorption, diffusion, trapping, embrittlement, phase stability, and membrane transport in a range of technologically relevant materials, including AB2 alloys, BCC and FCC alloys, and metallic glasses. A particular emphasis will be placed on integrating first-principles simulations, statistical mechanics, and machine learning with high-throughput and automated experimental efforts. The postdoctoral researcher will work closely with computational scientists, experimental collaborators, automated experimentation laboratories, and industry partners to accelerate the discovery of high-performance materials for energy and national security applications.

The successful candidate may contribute to projects involving the following key research areas:

• Hydrogen storage materials with improved capacity, reversibility, thermodynamics, and impurity tolerance.
• AB2-type alloys for hydrogen storage or separation, including materials with enhanced tolerance to CO2 and other gas impurities.
• High-performance and lower-cost BCC and FCC alloy membranes for hydrogen purification and separations.
• Metallic glass membranes for selective hydrogen transport.
• Hydrogen solubility, diffusivity, permeability, and surface chemistry in metal alloys and intermetallics.
• Development of computational workflows for high-throughput materials screening.
• Integration of density functional theory, thermodynamic modeling, statistical mechanics, and machine learning.
• Collaboration with experimental teams performing automated synthesis, characterization, and performance testing.

On any given day you may be called on to:

• Perform first-principles calculations, including density functional theory, to study hydrogen-metal interactions.
• Develop computational models for hydrogen absorption, diffusion, phase stability, and transport in metallic systems.
• Apply statistical mechanics and thermodynamic modeling to connect atomistic calculations with experimentally relevant observables.
• Build and deploy machine learning models for materials property prediction, alloy design, and uncertainty-aware screening.
• Develop robust Python-based workflows for data processing, simulation automation, model training, and analysis.
• Collaborate closely with experimental researchers to interpret data and guide materials synthesis and testing.
• Work with industry partners to connect fundamental materials insights to practical hydrogen technology applications.
• Communicate research progress through technical reports, presentations, peer-reviewed publications, and collaborative meetings.
• Contribute to a team-oriented research environment spanning computation, experiment, and applied materials development.

Due to the nature of the work, the selected applicant must be able to work onsite.

• A Ph.D. in materials science, chemical engineering, mechanical engineering, physics, chemistry, applied mathematics, computer science, or a closely related field.
• Ability to obtain and maintain a DOE Q clearance

Strong candidates should have:

• Demonstrated experience with computational materials science methods.
• Experience performing density functional theory calculations.
• Strong programming skills in Python.
• Knowledge of thermodynamics, statistical mechanics, and/or atomistic modeling.
• Experience with machine learning methods for scientific or materials applications
• Experience working with experimental collaborators or industry partners.
• A strong publication record in computational materials science, hydrogen technologies, or materials informatics.

Experience with one or more of the following is also desired:

• Hydrogen storage materials, metal hydrides, or hydrogen separation membranes.
• Alloy theory, phase stability, defect thermodynamics, or diffusion modeling.
• High-throughput computational materials screening.
• Cluster expansion, Monte Carlo methods, molecular dynamics, or CALPHAD-based modeling.
• Machine learning interatomic potentials or surrogate models for materials properties.
• Active learning or Bayesian optimization for materials discovery.
• Modeling of gassolid interactions, impurity effects, surface reactions, or membrane transport.
• Automated workflows and computational tools such as VASP, Quantum ESPRESSO, GPAW, LAMMPS, ASE, pymatgen, atomate, FireWorks, custodian, scikit-learn, PyTorch, JAX, or related packages.

The Materials Physics department performs fundamental research on the structure and properties of materials to support programs in energy research, device physics, materials reliability, and national security. Specific activities include experimental and modeling studies of surface electronic structure and surface atomic structure and dynamics of metals, oxides, low dimensional semiconductors, and adsorbates; atomic-scale structure and composition analysis of metals, semiconductors, and composites; electrical and optical properties of low-dimensional semiconductors and optoelectronic devices; and the structural and electrochemical characterization of solid-state ionic materials. The Department supports internal and external sponsors and government agencies that seek solutions to problems of fundamental scientific inquiry, energy sustainability, and national defense.

This posting will be open for Application submissions for a minimum of three (3) calendar days, including the ‘posting date’. Sandia reserves the right to extend the posting date at any time.

Sandia is required by DOE to conduct a pre-employment drug test and background review that includes checks of personal references, credit, law enforcement records, and employment/education verifications. Applicants for employment need to be able to obtain and maintain a DOE Q-level security clearance, which requires U.S. citizenship. If you hold more than one citizenship (i.e., of the U.S. and another country), your ability to obtain a security clearance may be impacted.

Applicants offered employment with Sandia are subject to a federal background investigation to meet the requirements for access to classified information or matter if the duties of the position require a DOE security clearance. Substance abuse or illegal drug use, falsification of information, criminal activity, serious misconduct or other indicators of untrustworthiness can cause a clearance to be denied or terminated by DOE, resulting in the inability to perform the duties assigned and subsequent termination of employment.

All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, or veteran status and any other protected class under state or federal law.

If you have a Medical Portable Electronic Device (MedPED), such as a pacemaker, defibrillator, drug-releasing pump, hearing aids, or diagnostic equipment and other equipment for measuring, monitoring, and recording body functions such as heartbeat and brain waves, if employed by Sandia National Laboratories you may be required to comply with NNSA security requirements for MedPEDs.

If you have a MedPED and you are selected for an on-site interview at Sandia National Laboratories, there may be additional steps necessary to ensure compliance with NNSA security requirements prior to the interview date.

This postdoctoral position is a temporary position for up to one year, which may be renewed at Sandia’s discretion up to five additional years. The PhD must have been conferred within five years prior to employment.

Individuals in postdoctoral positions may bid on regular Sandia positions as internal candidates, and in some cases may be converted to regular career positions during their term if warranted by ongoing operational needs, continuing availability of funds, and satisfactory job performance.