Computational Materials Discovery Scientist

About QPIAI India Pvt. Ltd. QPIAI India Pvt. Ltd. is a next-generation technology company focused on Artificial Intelligence, Quantum Computing, and advanced IT innovation. At QPIAI, we believe that great ideas grow in the right environment. Our culture is built on flexibility, collaboration, and continuous learning, supported by a strong commitment to employee well-being and work–life balance. We provide a workplace that encourages creativity, fosters professional growth, and empowers people to take ownership of impactful projects. We are proud to be a company where innovation meets opportunity—where passionate professionals come together to solve complex challenges and shape the future of technology. With our rapid expansion across India, Dubai, and Singapore, we are actively looking for individuals who share our mission and vision. If you are driven by curiosity, inspired by cutting-edge technologies, and eager to contribute to a global tech journey, QPIAI is the place to grow, lead, and make a meaningful impact. Position Summary As a Computational Materials Discovery Scientist, you will work at the intersection of materials science, computational chemistry, condensed matter physics, quantum computing, and AI/ML. You will contribute to first-principles simulations, molecular and mesoscale modeling, materials informatics pipelines, and hybrid quantum–classical algorithms for accelerated discovery of materials, catalysts, semiconductors, polymers, and functional systems. This role is ideal for candidates who want to solve real scientific and industrial problems using DFT, MD, multiscale modeling, machine learning, and emerging quantum computing techniques, fully integrated with QpiAI’s AI and quantum computing platforms. Requirements Core Technical Skills 1. Computational & Quantum Mechanical Methods Electronic Structure & Quantum Methods Density Functional Theory (DFT) Ab initio Molecular Dynamics (AIMD) Time-dependent DFT (TDDFT) for excited states DFT+U for strongly correlated systems Hybrid functionals Hartree–Fock and post-HF methods (MP2, CCSD(T)) Molecular & Statistical Simulations Classical Molecular Dynamics (MD) Force-field development and validation Monte Carlo (MC) simulations Kinetic Monte Carlo (kMC) for surface reactions Machine Learning Force Fields ( Fairchem, Universal Forcefield, MACE) Multiscale & Mesoscale Modeling Hierarchical nano → micro → meso → macro modeling QM–MM and QM–continuum coupling Microkinetic modeling and reaction networks Phase-field modeling Free energy methods (umbrella sampling, metadynamics) 2. Materials Simulation & Electronic Structure Modeling Bulk and surface DFT calculations k-point convergence, basis set testing Band structure, DOS, PDOS Phonons and lattice dynamics Elastic constants, thermal & mechanical properties Defect formation energies Phase stability & convex hulls Slab models and adsorption energies Reaction pathways using NEB / CI-NEB Benchmarking & Validation Cross-code benchmarking across: DFT engines: VASP, Quantum ESPRESSO, CP2K, GPAW, CASTEP, Gaussian, ORCA MD engines: LAMMPS, GROMACS, NAMD ML potential 3. Materials Informatics & AI/ML Build ML models for materials & catalyst property prediction: Fairchem, Universal Forcefield, CGCNN, MEGNet, SchNet, e3nn, NequIP Transformer & foundation models for materials Curate datasets from: Materials Project, OQMD, NOMAD, JARVIS Develop ML surrogates for: Energy & force prediction Bandgap estimation Thermal & mechanical properties Catalyst screening & ranking Integrate ML pipelines with: DFT / MD workflows Quantum simulation pipelines 4. Simulation Workflow Engineering Build reproducible, automated workflows in Python for: High-throughput materials screening DFT–MD–CG-Mesoscale simulation pipelines Data extraction & post-processing Develop modular tools for: Structure parsing (CIF, POSCAR, XYZ, PDB) Geometry builders & surface generators Parameters generations Visualization (band structure, DOS, phonons, trajectories) Deploy workflows on: HPC clusters Cloud platforms (AWS, GCP) Containerized environments (Docker) 5. Research, Collaboration & Documentation Conduct literature reviews in: Computational materials Catalysis Semiconductors Alloy and Ceramics Polymers Quantum algorithms Design, execute, and analyze numerical experiments Prepare: Technical reports Internal whitepapers Presentations and datasets Collaborate closely with: Quantum hardware teams Algorithm developers AI/ML engineers Specialization Tracks A. DFT & Electronic Structure Specialization Advanced XC functional selection & benchmarking Strongly correlated systems (DFT+U, Hubbard models) Excited-state calculations (TDDFT, GW – exposure preferred) Defects, surfaces, and interfaces Electronic transport & conductivity modeling B. Molecular Dynamics & Classical Simulations Classical MD simulations (LAMMPS, GROMACS) Force-field parameterization & validation Free energy calculations Reactive force fields (ReaxFF) ML-accelerated MD workflows Parameter generation for coarse-grained simulations C. Catalysis Specialization Heterogeneous, homogeneous & electro-catalysis Reaction pathway identification Transition state searches (NEB, CI-NEB) Adsorption energies & surface thermodynamics Microkinetic modeling Applications: OER, ORR, HER Photocatalysis Single-atom & nanocluster catalysts D. Polymers & Soft Matter Specialization DFT-based parameter extraction for polymers Multiscale polymer modeling (DFT, AA, CG) Dissipative Particle Dynamics (DPD) Monte Carlo Simulations Polymer blends, Polymer nanocomposites, surfactants, colloids Polymerization, degradation, crosslinking, morphology and aging studies Integration of DFT → MD → DPD→Phase field simulations pipelines E. Quantum Computing for Materials Simulation Map materials Hamiltonians to qubits: Jordan–Wigner, Bravyi–Kitaev, parity mappings Work on quantum algorithms including: VQE for correlated materials Subspace Quantum Diagonalization (SQD) qEOM for excited states Quantum Phase Estimation QITE / Quantum Monte Carlo Analyze: Qubit requirements Circuit depth Noise & error budgets Design material-specific ansätze for NISQ devices and simulators Software & Programming Skills DFT Codes: VASP, QE, CP2K, Gaussian, ORCA, CASTEP, ADF MD Codes: LAMMPS, GROMACS, NAMD, AMBER Visualization: VMD, VESTA, OVITO, Materials Studio, ASE Programming: Python (mandatory), Bash ML: PyTorch, TensorFlow, scikit-learn Infrastructure: HPC, MPI, Docker, Git, AWS / GCP Soft Skills Strong analytical and first-principles thinking Ability to design reproducible scientific workflows Clear scientific communication High ownership and curiosity-driven research mindset) Educational Qualifications PhD (or pursuing PhD for intern role) in Chemistry, Materials Science, Chemical Engineering, Physics, Computational Science or related STEM field Strong foundation in Physical chemistry, Quantum mechanics, Statistical mechanics & thermodynamics Specialization in computational chemistry / materials modeling strongly preferred Preferred Qualifications Publications or strong computational project portfolio Experience with HPC & large-scale simulations Prior work in: Materials discovery, Catalysis, Semiconductor, Polymer modeling, ML-driven materials science Exposure to quantum algorithms or hybrid quantum–classical workflows