Postdoc: AI-Driven Quantum Catalysis & ML Models

Argonne National Laboratory; Lemont (IL); On-site; USD 73,000 - 121,000; Full time

Job summary

Argonne National Laboratory’s Chemical Sciences and Engineering Division invites applications for a Postdoctoral Appointee to advance research at the intersection of computational catalysis, quantum chemistry, and AI. You will perform first-principles calculations and develop AI models to accelerate discovery in heterogeneous catalysis.

The position emphasizes collaboration with experimental teams, strong communication, and the ability to translate computational predictions into mechanistic

Qualifications

• Recent or soon-to-be-completed PhD in chemistry, physics, chemical engineering, or a closely related field.
• Strong foundation in quantum mechanics and electronic structure theory.
• Demonstrated expertise in developing advanced AI/ML models for physics, chemistry, or materials.
• Experience with periodic simulation codes such as VASP.
• Proficiency in Python programming.
• Excellent written and oral communication skills.
• Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork.

Responsibilities

• Conduct computational studies of catalytic active sites and reaction pathways using quantum mechanical methods.
• Develop advanced AI/ML models, including agentic and physics-aware approaches, for applications in catalysis.
• Collaborate closely with experimental teams to connect computational predictions with experimental observations.
• Communicate research results effectively through publications, presentations, and team interactions.

Skills

Quantum chemistry

AI/ML models

Python programming

VASP

Communication skills

Education

PhD in chemistry/physics/chemical engineering

Tools

VASP

Job description

Argonne National Laboratory’s Chemical Sciences and Engineering Division invites applications for a Postdoctoral Appointee to advance research at the intersection of computational catalysis, quantum chemistry, and AI. You will perform first-principles calculations and develop AI models to accelerate discovery in heterogeneous catalysis.

The position emphasizes collaboration with experimental teams, strong communication, and the ability to translate computational predictions into mechanistic