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About the Role
We are seeking a Computational Materials Scientist to lead first-principles modeling of structural stability, lattice dynamics, and thermodynamic properties. This role is central to our materials discovery pipeline, generating reference data and physical insights for MLIP training and our simulation stack. You will join a dynamic team of experts, contributing to cutting-edge computational methods and making a significant impact in materials science.
Responsibilities
- Perform ab initio studies of ordered and disordered crystal structures, computing free energies and characterizing entropy contributions across temperatures.
- Assess structural stability of various phases and perform ab initio molecular dynamics simulations, applying free energy methods to study structure-magnetism interplay.
- Characterize anharmonic effects beyond the harmonic approximation using SSCHA, TDEP, or related methods.
- Generate high-quality DFT reference datasets, forces, stresses, energies, phonon dispersions, and magnetic moments for MLIP training.
- Collaborate with the ML engineering team to design efficient training sets and with the modeling team to provide converged ab initio parameters.
- Maintain and improve DFT workflow infrastructure, including automation, convergence protocols, and data management.
Requirements
- PhD in computational physics, materials science, chemistry, or a closely related field.
- Strong hands-on experience with DFT for solid-state systems (VASP, Quantum Espresso, GPAW, or equivalent).
- Experience with phonon calculations and lattice dynamics (DFPT, finite-difference approaches, Phonopy, or similar).
- Familiarity with molecular dynamics (ab initio or classical) and free energy methods.
- Ability to understand, derive, and numerically implement analytical physical formulae.
- Evidence of significant research impact through publications on computational materials science, DFT, lattice dynamics, magnetism, or related technical disciplines.
Skills
- DFT
- Phonon calculations
- Lattice dynamics
- Molecular dynamics
- Free energy methods
- Ab initio studies
- SSCHA
- TDEP
- VASP
- Quantum Espresso
- GPAW
- DFPT
- Phonopy
- Spin-polarised DFT
- Anharmonic methods
- Special quasi-random structures (SQS)
- Cluster expansion
- AiiDA
- Fireworks
Location
- London-based
Work Type
- Flexible approach to how and where you work
Experience Level
- PhD
Education Level
- PhD
Salary/Compensations
- Competitive salary
Benefits
- Generous equity
- Benefits
About the Company
- Diffractive is building the AI Material Scientist that autonomously learns from real-world experimentation to push the boundaries of scientific discovery.
- We're early, moving fast, and working on problems that genuinely matter.
- You'll join a small, high-calibre team where your work has real impact from day one.
- We offer competitive salary, generous equity and benefits.
- You'll have a real stake in what you build and in the company's overall success.
Equal Opportunity
- Diffractive is an equal opportunities employer.
- We are committed to creating an inclusive environment for all employees and welcome applications from people of all backgrounds, experiences, and identities.
- If you require any adjustments or accommodations at any point during the interview process please let us know - we will be happy to help.