Computational Materials Scientist

Description

Looking for an opportunity to make an impact and grow your career?

The Leidos Research Support Team supporting the National Energy Technology Laboratory (NETL) is seeking a Computational Materials Scientist to join our team! This opportunity will allow side by side execution of research with world-class scientists and engineers using state of the art equipment to contribute to new areas of basic and applied research.

The Computational Materials Scientist will be part of a distributed, multidisciplinary team working on the cutting edge of high-performance computing (HPC) and structural materials development. This work specifically supports fundamental understanding of hydrogen impact on structural and mechanical properties of metals and alloys at elevated temperatures and the design and development of low-cost high-performance materials for extreme environments using multiscale computational modeling and machine learning. The overarching goal is to address potential consequences of shifting from natural gas to hydrogen on the environmental degradation of post-combustion metallic components in power generation turbines. Efforts in this task will further lay the groundwork for the design of cost-effective materials broadly focused on enabling DOE efficiency and emissions targets.

Primary Responsibilities:

• Perform density functional theory (DFT) calculations to develop machine learning interatomic potentials (MLIAPs) for steels in presence of hydrogen considering a variety of crystal defects including vacancy, stacking faults, dislocation, surface, grain boundary, and interface.
• Perform LAMMPS molecular dynamics & Monte Carlo simulations to predict structural evolution (including phase transformations) and mechanical behavior (including creep, fatigue and their interaction) in steels in presence of hydrogen as a function of alloy composition, temperature, stress, and hydrogen pressure.

Required Education, Experience & Other:

• Ph.D. degree in Materials Science, Chemical or Mechanical Engineering, or a related field with 4+ years experience
• Extensive research experience in first principles DFT calculations to predict thermodynamic, kinetic, and mechanical properties of crystalline solids using VASP package is required. Direct research experience in crystal defects in metals such as dislocations and interfaces are highly desirable
• Excellent computer programming and coding skills such as Python, C/C++, Fortran, Linux script, etc., and demonstrated proficiency in supercomputers and Linux system are required
• Demonstrated proficiency in supervised and unsupervised machine learning models, and experience in data curation and analysis. Experience in multi-objective optimization is highly desirable
• Excellent record of peer-reviewed quality publications
• Excellent oral and written communication skills
• Ability to work independently and with minimum supervision, and ability to work effectively as a part of a team in a multi-disciplinary environment and interact with people with a variety of expertise
• Must be able to meet the requirements for gaining access to work on the NETL campus
• Must be willing to travel periodically as business needs require (approximately 4 times a year)

Preferred Qualifications:

• Strong research experience developing MLIAPs for metals and alloys considering crystal defects.
• Strong research experience in molecular dynamics & Monte Carlo using LAMMPS to simulate structural evolution and mechanical behavior such as creep and fatigue of metals and alloys at elevated temperatures.
• Strong research experience in supervised and unsupervised machine learning to predict composition-processing-microstructure-properties relationship of structural materials.

Salary Range for this position is $115K to $135K

Pay Range: Pay Range $97,500.00 - $176,250.00

The Leidos pay range for this job level is a general guideline only and not a guarantee of compensation or salary. Additional factors considered in extending an offer include (but are not limited to) responsibilities of the job, education, experience, knowledge, skills, and abilities, as well as internal equity, alignment with market data, applicable bargaining agreement (if any), or other law.