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 SummaryAs 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.RequirementsCore Technical Skills1. Computational & Quantum Mechanical MethodsElectronic Structure & Quantum MethodsDensity Functional Theory (DFT) Ab initio Molecular Dynamics (AIMD)Time-dependent DFT (TDDFT) for excited statesDFT+U for strongly correlated systemsHybrid functionals Hartree–Fock and post-HF methods (MP2, CCSD(T))Molecular & Statistical SimulationsClassical Molecular Dynamics (MD)Force-field development and validationMonte Carlo (MC) simulationsKinetic Monte Carlo (kMC) for surface reactionsMachine Learning Force Fields ( Fairchem, Universal Forcefield, MACE)Multiscale & Mesoscale ModelingHierarchical nano → micro → meso → macro modelingQM–MM and QM–continuum couplingMicrokinetic modeling and reaction networksPhase-field modelingFree energy methods (umbrella sampling, metadynamics)2. Materials Simulation & Electronic Structure ModelingBulk and surface DFT calculationsk-point convergence, basis set testingBand structure, DOS, PDOSPhonons and lattice dynamicsElastic constants, thermal & mechanical propertiesDefect formation energiesPhase stability & convex hullsSlab models and adsorption energiesReaction pathways using NEB / CI-NEBBenchmarking & ValidationCross-code benchmarking across:DFT engines: VASP, Quantum ESPRESSO, CP2K, GPAW, CASTEP, Gaussian, ORCAMD engines: LAMMPS, GROMACS, NAMDML potential3. Materials Informatics & AI/MLBuild ML models for materials & catalyst property prediction:Fairchem, Universal Forcefield, CGCNN, MEGNet, SchNet, e3nn, NequIPTransformer & foundation models for materialsCurate datasets from:Materials Project, OQMD, NOMAD, JARVISDevelop ML surrogates for:Energy & force predictionBandgap estimationThermal & mechanical propertiesCatalyst screening & rankingIntegrate ML pipelines with:DFT / MD workflowsQuantum simulation pipelines4. Simulation Workflow EngineeringBuild reproducible, automated workflows in Python for:High-throughput materials screeningDFT–MD–CG-Mesoscale simulation pipelinesData extraction & post-processingDevelop modular tools for:Structure parsing (CIF, POSCAR, XYZ, PDB)Geometry builders & surface generatorsParameters generations Visualization (band structure, DOS, phonons, trajectories)Deploy workflows on:HPC clustersCloud platforms (AWS, GCP)Containerized environments (Docker)5. Research, Collaboration & DocumentationConduct literature reviews in:Computational materialsCatalysisSemiconductorsAlloy and CeramicsPolymersQuantum algorithmsDesign, execute, and analyze numerical experimentsPrepare:Technical reportsInternal whitepapersPresentations and datasetsCollaborate closely with:Quantum hardware teamsAlgorithm developersAI/ML engineersSpecialization Tracks A. DFT & Electronic Structure SpecializationAdvanced XC functional selection & benchmarkingStrongly correlated systems (DFT+U, Hubbard models)Excited-state calculations (TDDFT, GW – exposure preferred)Defects, surfaces, and interfacesElectronic transport & conductivity modelingB. Molecular Dynamics & Classical SimulationsClassical MD simulations (LAMMPS, GROMACS)Force-field parameterization & validationFree energy calculationsReactive force fields (ReaxFF)ML-accelerated MD workflowsParameter generation for coarse-grained simulationsC. Catalysis SpecializationHeterogeneous, homogeneous & electro-catalysisReaction pathway identificationTransition state searches (NEB, CI-NEB)Adsorption energies & surface thermodynamicsMicrokinetic modelingApplications:OER, ORR, HERPhotocatalysisSingle-atom & nanocluster catalystsD. Polymers & Soft Matter SpecializationDFT-based parameter extraction for polymersMultiscale polymer modeling (DFT, AA, CG)Dissipative Particle Dynamics (DPD)Monte Carlo SimulationsPolymer blends, Polymer nanocomposites, surfactants, colloidsPolymerization, degradation, crosslinking, morphology and aging studiesIntegration of DFT → MD → DPD→Phase field simulations pipelinesE. Quantum Computing for Materials SimulationMap materials Hamiltonians to qubits:Jordan–Wigner, Bravyi–Kitaev, parity mappingsWork on quantum algorithms including:VQE for correlated materialsSubspace Quantum Diagonalization (SQD)qEOM for excited statesQuantum Phase EstimationQITE / Quantum Monte CarloAnalyze:Qubit requirementsCircuit depthNoise & error budgetsDesign material-specific ansätze for NISQ devices and simulatorsSoftware & Programming SkillsDFT Codes: VASP, QE, CP2K, Gaussian, ORCA, CASTEP, ADFMD Codes: LAMMPS, GROMACS, NAMD, AMBERVisualization: VMD, VESTA, OVITO, Materials Studio, ASEProgramming: Python (mandatory), BashML: PyTorch, TensorFlow, scikit-learnInfrastructure: HPC, MPI, Docker, Git, AWS / GCPSoft SkillsStrong analytical and first-principles thinkingAbility to design reproducible scientific workflowsClear scientific communicationHigh ownership and curiosity-driven research mindset]Educational QualificationsPhD (or pursuing PhD for intern role) in Chemistry, Materials Science, Chemical Engineering, Physics, Computational Science or related STEM fieldStrong foundation in Physical chemistry, Quantum mechanics, Statistical mechanics & thermodynamicsSpecialization in computational chemistry / materials modeling strongly preferredPreferred QualificationsPublications or strong computational project portfolioExperience with HPC & large-scale simulationsPrior work in: Materials discovery, Catalysis, Semiconductor, Polymer modeling, ML-driven materials scienceExposure to quantum algorithms or hybrid quantum–classical workflows