Computational Materials Discovery Intern

Position Summary As a Computational Materials Discovery Intern, you will work at the intersection of materials science, computational physics, quantum chemistry, quantum computing, and AI/ML. You will contribute to simulation workflows, property prediction pipelines, materials dataset generation, and hybrid quantum–classical algorithms for accelerated materials design—integrated with QpiAI’s quantum computing systems and AI platforms. This role is ideal for candidates who want to work on real scientific problems involving electronic structure modeling, materials screening, generative AI for materials, and quantum-accelerated materials simulation. Key Responsibilities 1. Materials Simulation & Electronic Structure Modeling Perform classical materials simulations using: DFT (plane-wave & localized basis set methods) Pseudopotentials, k-point sampling, band structure and DOS calculations Phonon calculations, elastic constants, mechanical/thermal property prediction Molecular dynamics (MD) and Monte Carlo sampling Execute materials workflows for: Crystal optimization Defect formation energies Adsorption energies Reaction pathways (NEB) Phase stability diagrams Surface modeling (slabs) Catalyst descriptors (d-band center, charge transfer etc.) Benchmark performance across classical solvers (VASP, Quantum ESPRESSO, CP2K, GPAW, CASTEP, LAMMPS). 2. Materials Informatics & AI/ML for Materials Build machine learning models for materials property prediction: Graph neural networks (CGCNN, MEGNet, SchNet) E(3)-equivariant networks Transformers for crystalline materials Curate and clean datasets from: Materials Project OQMD NOMAD JARVIS Accelerate materials workflows using ML surrogates for: Energy prediction Bandgap estimation Mechanical/thermal property prediction Catalyst screening Battery & semiconductor material exploration Integrate ML pipelines with classical & quantum simulation workflows. 3. Quantum Computing for Materials Simulation Map materials-related Hamiltonians to qubit representations (JW, BK, Parity mapping). Work with quantum algorithms for materials simulation: VQE for strongly correlated materials qEOM for excited states Quantum phase estimation for band structure Quantum Monte Carlo or QITE for condensed-matter systems Develop and analyze quantum resources (qubits, depth, error budgets) for materials use cases. Prototype material-specific ansätze for QPUs and simulators. 4. Simulation Workflow Engineering Build reproducible workflows in Python for: High-throughput materials screening Automated DFT/MD/PES pipelines Data extraction & post-processing Implement modular tools for: Structure parsing (CIF, POSCAR, XYZ) Geometry builders Visualization (band structure, DOS, phonon spectra, surfaces) Integrate simulation modules into QpiAI’s AI/quantum platform. 5. Research, Experimentation & Documentation Conduct literature surveys on computational materials, materials informatics, and quantum algorithms. Run experiments, compare results, and document scientific insights. Prepare technical reports, presentations, and datasets for internal R&D. Collaborate with QpiAI’s quantum hardware, algorithms, and ML teams. Required Skills Technical Skills Strong understanding of materials science, solid-state physics, and computational modelling. Hands-on experience with DFT tools (VASP, QE, CP2K, GPAW, CASTEP) or MD engines (LAMMPS, GROMACS). Python programming for scientific workflows (NumPy, ASE, pymatgen, Matminer). Familiarity with quantum chemistry or many-body methods (HF, MP2, CC, Hubbard models). Understanding of quantum computing concepts (Hamiltonians, ansätze, variational algorithms). Exposure to ML frameworks (PyTorch, TensorFlow) and materials ML libraries (Matminer, CGCNN). Domain Knowledge Crystal structures, defects, band theory, PES, phonons, surfaces/interfaces. Battery/materials for energy applications (bonus). Catalysts, semiconductors, superconductors (bonus). Soft Skills Strong scientific thinking and analytical skills. Ability to write clean, reproducible code and maintain careful documentation. Passion for materials innovation using AI and quantum technologies. Preferred Qualifications Pursuing M.Tech/M.Sc/PhD in Materials Science, Physics, Chemistry, Chemical Engineering, Nanotechnology, Quantum Computing, or related fields. Prior projects in materials simulation, computational chemistry, or ML-based materials discovery. Experience with high-performance computing environments. Publications or strong project portfolio.