2026 New College Grad Computational Chemist Materials Scientist (Machine Learning – Reactive MLIPs) Doctorate Degree

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.

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We are seeking a highly skilled Computational Materials Scientist with deep expertise in machine learning for atomistic modeling specifically in reactive machine-learned interatomic potentials (MLIPs). This role focuses on developing and deploying ML-driven models capable of accurately capturing bond breaking bond formation and complex chemical reactions enabling predictive simulations at near first-principles accuracy with significantly improved scalability.

The ideal candidate will combine physics-based understanding advanced machine learning techniques and strong analytical reasoning to solve challenging problems in materials design and process development.

Key Responsibilities

• 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 metadynamics) 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 structureproperty and structurereactivity relationships.
• Collaborate cross-functionally with experimental process and device teams to guide materials and process optimization.

Required Qualifications

• 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 TensorFlow).
• 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.

Core Technical Competencies

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

Analytical & Reasoning Requirements

• 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.

Preferred Qualifications

• 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 (FireWorks AiiDA etc.)
• Exposure to graph neural networks (GNNs) and equivariant architectures.
• Industry experience in materials development or process modeling.

Additional Information

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The salary offered to a selected candidate will be based on multiple factors including location hire grade job-related knowledge skills experience and with consideration of internal equity of our current team addition to a comprehensive benefits package candidates may be eligible for other forms of compensation such as participation in a bonus and a stock award program as applicable.

For all sales roles the posted salary range is the Target Total Cash (TTC) range for the role which is the sum of base salary and target bonus amount at 100% goal achievement.

Applied Materials is an Equal Opportunity Employer. Qualified applicants will receive consideration for employment without regard to race color national origin citizenship ancestry religion creed sex sexual orientation gender identity age disability veteran or military status or any other basis prohibited by law.

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