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.
Requirements
Core Technical Skills
- 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)
- 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
- 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
- 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)
- 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
- 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
- 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
- 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
- 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
- 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
Skills: ml,dft,materials,simulations,modeling
