Job Description
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 Qpi AI’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 (k MC) 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, CP2 K, 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, Sch Net, e3nn, Nequ IP
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 (Reax FF)
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)
q EOM 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, CP2 K, Gaussian, ORCA, CASTEP, ADF
MD Codes: LAMMPS, GROMACS, NAMD, AMBER
Visualization: VMD, VESTA, OVITO, Materials Studio, ASE
Programming: Python (mandatory), Bash
ML: Py Torch, Tensor Flow, 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
Ph D (or pursuing Ph D 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
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 Qpi AI’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 (k MC) 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, CP2 K, 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, Sch Net, e3nn, Nequ IP
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 (Reax FF)
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)
q EOM 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, CP2 K, Gaussian, ORCA, CASTEP, ADF
MD Codes: LAMMPS, GROMACS, NAMD, AMBER
Visualization: VMD, VESTA, OVITO, Materials Studio, ASE
Programming: Python (mandatory), Bash
ML: Py Torch, Tensor Flow, 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
Ph D (or pursuing Ph D 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
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