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Computational Materials Scientist

Woburn, MA · On-site +1

$180K - $200K/yr

Atomistic Modeling & Simulation * Conduct and oversee DFT (Density Functional Theory), MD (Molecular Dynamics), and QM (Quantum Mechanics) simulations of battery components, including electrolytes ...

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Atomistic Simulations information

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$123.4K

$190.5K

How much do atomistic simulations jobs pay per year?

As of Aug 29, 2026, the average yearly pay for atomistic simulations in the United States is $123,399.00, according to ZipRecruiter salary data. Most workers in this role earn between $92,000.00 and $146,500.00 per year, depending on experience, location, and employer.

What are atomistic simulations?

Atomistic simulations are computational methods used to model and study the behavior of materials and molecules at the atomic scale. By simulating the interactions between individual atoms, these techniques help scientists understand material properties, chemical reactions, and biological processes. Common approaches include molecular dynamics (MD) and Monte Carlo simulations, which predict how atoms move and interact over time. Atomistic simulations are widely used in chemistry, physics, materials science, and biology to complement experimental research and design new materials.

What are the key skills and qualifications needed to thrive as an atomistic simulation scientist, and why are they important?

To excel as an Atomistic Simulation Scientist, you need a strong background in physics, chemistry, or materials science, often supported by a relevant advanced degree and experience in computational modeling. Familiarity with simulation software such as LAMMPS, VASP, or GROMACS, as well as programming languages like Python or Fortran, is essential. Strong analytical thinking, problem-solving abilities, and effective communication skills help you interpret results and collaborate with interdisciplinary teams. These competencies are crucial for designing accurate simulations, deriving meaningful insights, and advancing research or product development.

What are some common challenges faced by professionals working in atomistic simulations, and how can they be addressed?

Professionals in atomistic simulations often encounter challenges such as managing large datasets, ensuring the accuracy of computational models, and optimizing simulation performance. Collaborating closely with interdisciplinary teams—including experimentalists, computational scientists, and software engineers—helps overcome these barriers. Staying updated with the latest software tools and high-performance computing resources is also essential for efficient workflow. Regularly validating simulation results against experimental data enhances credibility and reliability in findings.

What is the difference between Atomistic Simulations vs Computational Chemist?

AspectAtomistic SimulationsComputational Chemist
Required CredentialsBachelor's or Master's in Chemistry, Physics, or related fields; knowledge of simulation softwareBachelor's or Master's in Chemistry, Chemical Engineering, or related; strong computational skills
Work EnvironmentResearch labs, academic institutions, industry R&DResearch labs, pharmaceutical companies, academia
Industry UsageMaterial science, nanotechnology, molecular modelingDrug discovery, material design, chemical analysis

Atomistic Simulations involve modeling materials or molecules at the atomic level using computational methods. Computational Chemists apply these techniques to solve chemical problems, often utilizing atomistic simulations as part of their work. While both roles require similar educational backgrounds and work environments, atomistic simulations focus specifically on the simulation techniques, whereas computational chemists may also include data analysis and experimental design.

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Atomistic Simulations jobs near you
Infographic showing various Atomistic Simulations job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 71% Full Time, 26% Part Time, and 2% Contract. Highlights an 68% Physical, 4% Hybrid, and 28% Remote job distribution, with an average salary of $123,399 per year, or $59.3 per hour.

Research Scientist I/II, Computational Organic Electronics

Lila Sciences

Cambridge, MA • Hybrid

Full-time

Posted 7 days ago

Job description

Your Impact at LILA

Your role will involve applying computational methods and AI to accelerate the discovery and design of organic electronics materials. You will use first-principles modeling, atomistic simulations, scientific machine learning, and agentic AI systems to investigate structure-property relationships in organic and hybrid materials relevant to photovoltaics, semiconductors, optoelectronics, or electronic devices.

You will work at the intersection of physics-based simulation, AI/ML, and autonomous scientific workflows. The focus is on using computational insight to identify promising materials, explain structure-property relationships, guide optimization, and help agents reason over simulation and experimental data in scientifically grounded ways.

This is a hands-on research role for someone who can connect deep organic electronics and computational materials expertise with practical impact for customer-facing scientific programs. You will collaborate with computational scientists, AI researchers, software engineers, and experimental teams to turn simulations, models, and scientific reasoning into actionable hypotheses and discovery workflows.

What You'll Be Building

  • Apply computational modeling and AI for materials discovery and design of organic semiconductors, photovoltaic materials, molecular and polymeric electronic materials, and organic electronic devices.
  • Model charge transport, excited-state behavior, morphology-property relationships, and other fundamental mechanisms that influence organic electronic device performance.
  • Connect simulation outputs to experimental observations and develop workflows that close the loop between computation and experiment.
  • Build predictive models from computational and experimental data to guide materials selection and optimization.
  • Analyze simulation and experimental data to generate actionable materials hypotheses.
  • Partner with ML, software, and experimental teams on discovery workflows.
  • Communicate physical insights, model limitations, and recommendations to collaborators.

What You'll Need to Succeed

  • PhD or equivalent experience in Materials Science, Chemistry, Chemical Engineering, Mechanical Engineering, Physics, or a related field.
  • Strong foundation in computational materials science and chemistry, including electronic structure methods and large-scale atomistic simulations.
  • Deep understanding of organic semiconductors, organic electronics, photovoltaics, optoelectronic materials, charge transport, or related device-relevant materials systems.
  • Experience applying first-principles, molecular simulations, or general atomistic methods to materials discovery, optimization, or understanding.
  • Ability to connect molecular, morphological, and electronic structure features to device-relevant properties.
  • Strong programming skills in Python and scientific computing workflows.

Bonus Points For

  • Experience studying organic photovoltaics, organic semiconductors, polymer electronics, molecular electronics, perovskite-organic interfaces, or related materials systems.
  • Experience applying AI/ML to computational materials science, molecular simulations, or other physics-based simulations.
  • Strong familiarity with agentic AI systems, autonomous scientific workflows, or simulation-aware agents.
  • Experience integrating computational predictions with experimental characterization, device measurements, or closed-loop optimization workflows.
  • Familiarity with charge transport modeling, excited-state calculations, morphology generation, coarse-graining, and/or multiscale and multiphysics simulations.
  • Ability to communicate physical insight, uncertainty, and model limitations to cross-functional collaborators.

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