College Grad - Computational Chemist​/Materials Scientist; Machine Learning – Reactiv

Who We Are

Applied Materials is a global leader in materials engineering solutions used to produce virtually every new chip and advanced display in the world. We design, build and service cutting‑edge equipment that helps our customers manufacture display and semiconductor chips – the brains of devices we use every day. As the foundation of the global electronics industry, Applied enables the exciting technologies that literally connect our world – like AI and IoT.

If you want to push the boundaries of materials science and engineering to create next generation technology, join us to deliver material innovation that changes the world.

Salary: $ - $

Location:

Santa Clara, CA

You’ll benefit from a supportive work culture that encourages you to learn, develop, and grow your career as you take on challenges and drive innovative solutions for our customers. We empower our team to push the boundaries of what is possible—while learning every day in a supportive leading global company. Visit our Careers website to learn more.

At Applied Materials, we care about the health and wellbeing of our employees. We’re committed to providing programs and support that encourage personal and professional growth and care for you at work, at home, or wherever you may go. Learn more about our benefits.

• Develop, train, and deploy reactive MLIPs to model chemical reactions, interfacial processes, and dynamic material behavior.
• Build ML models capable of predicting energies, forces, and reaction pathways with near DFT‑level accuracy.
• Generate and curate high‑quality training datasets from DFT and other first‑principles methods.
• Design and implement active learning workflows to iteratively improve model robustness and coverage of configuration space.
• Integrate MLIPs with molecular dynamics (MD) to simulate:
  • Reactive processes
  • Diffusion and transport
  • Oxidation/reduction
  • Surface and interface evolution
• Apply enhanced sampling techniques (e.g., NEB, meta dynamics) in combination with ML models for reaction pathway exploration.
• Develop automated simulation pipelines and scalable workflows for high‑throughput studies.
• Analyze large datasets to extract structure–property and structure–reactivity relationships.
• Collaborate cross‑functionally with experimental, process, and device teams to guide materials and process optimization.

• Ph.D. in Materials Science, Physics, Chemistry, or related field.
• Demonstrated expertise in reactive machine‑learned interatomic potentials (MLIPs) capable of modeling bond breaking and formation.
• Hands‑on experience with one or more MLIP frameworks:
  • MACE, NequIP, GAP, SNAP, DeepMD, or equivalent
• Strong background in first‑principles methods (DFT) and atomistic simulations (MD).
• Proficiency in Python and ML frameworks (PyTorch, Tensor Flow).
• Experience working in HPC environments and handling large‑scale simulations.
• Proven ability in dataset generation, labeling strategies, and model validation for ML‑based atomistic models.

• Reactive MLIP development and deployment
• Machine learning for atomistic simulations
• Molecular dynamics and reaction modeling
• Materials informatics and data pipelines
• High‑performance scientific computing

• Strong analytical, logical reasoning, and quantitative problem‑solving skills.
• Demonstrated ability to:
  • Diagnose and debug ML model failures and training instabilities
  • Critically evaluate model predictions against physical principles
  • Ensure physical consistency, transferability, and robustness of simulations
  • Identify gaps in training data and design targeted data acquisition strategies
• Ability to translate complex physical phenomena into tractable computational models.

• Experience in reactive systems, including:
  • Surface chemistry
  • Catalysis
  • Oxidation/reduction reactions
  • Semiconductor or interface materials
• Familiarity with uncertainty quantification, Bayesian methods, and active learning.
• Experience with:
  • ASE, LAMMPS, VASP, or similar tools
  • Workflow frameworks (Fire Works, AiiDA, etc.)
• Exposure to graph neural networks (GNNs) and equivariant architectures.
• Industry experience in…