Computational Materials Discovery Intern

Position SummaryAs 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 Responsibilities1. Materials Simulation & Electronic Structure ModelingPerform classical materials simulations using:DFT (plane-wave & localized basis set methods)Pseudopotentials, k-point sampling, band structure and DOS calculationsPhonon calculations, elastic constants, mechanical/thermal property predictionMolecular dynamics (MD) and Monte Carlo samplingExecute materials workflows for:Crystal optimizationDefect formation energiesAdsorption energiesReaction pathways (NEB)Phase stability diagramsSurface 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 MaterialsBuild machine learning models for materials property prediction:Graph neural networks (CGCNN, MEGNet, SchNet)E(3)-equivariant networksTransformers for crystalline materialsCurate and clean datasets from:Materials ProjectOQMDNOMADJARVISAccelerate materials workflows using ML surrogates for:Energy predictionBandgap estimationMechanical/thermal property predictionCatalyst screeningBattery & semiconductor material explorationIntegrate ML pipelines with classical & quantum simulation workflows.3. Quantum Computing for Materials SimulationMap materials-related Hamiltonians to qubit representations (JW, BK, Parity mapping).Work with quantum algorithms for materials simulation:VQE for strongly correlated materialsqEOM for excited statesQuantum phase estimation for band structureQuantum Monte Carlo or QITE for condensed-matter systemsDevelop and analyze quantum resources (qubits, depth, error budgets) for materials use cases.Prototype material-specific ansätze for QPUs and simulators.4. Simulation Workflow EngineeringBuild reproducible workflows in Python for:High-throughput materials screeningAutomated DFT/MD/PES pipelinesData extraction & post-processingImplement modular tools for:Structure parsing (CIF, POSCAR, XYZ)Geometry buildersVisualization (band structure, DOS, phonon spectra, surfaces)Integrate simulation modules into QpiAI’s AI/quantum platform.5. Research, Experimentation & DocumentationConduct 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 SkillsTechnical SkillsStrong 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 KnowledgeCrystal structures, defects, band theory, PES, phonons, surfaces/interfaces.Battery/materials for energy applications (bonus).Catalysts, semiconductors, superconductors (bonus).Soft SkillsStrong 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 QualificationsPursuing 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.