What We’re Looking For
We are looking for a Computational Materials Scientist to drive our first-principles modelling of structural stability, lattice dynamics, and thermodynamic properties. You will sit at the heart of our materials discovery pipeline, producing the reference data and physical understanding that feeds both our MLIP training and our broader simulation stack.
You will be joining a small, highly ambitious team of world-class materials scientists, engineers, and AI researchers. We move fast and value people who are energised by that.
This is a role for someone who has a deep understanding of computational material science, is excited about pushing the boundaries of computational methods, and make a meaningful contribution to material science
What You’ll Do
Perform
ab initio
studies of ordered and disordered crystal structures, computing free energies and characterising the interplay between various entropy contributions across temperatures.
Assess structural stability of various phases and perform
ab initio
molecular dynamics simulations, applying free energy methods to study the interplay between structure and magnetism.
Characterise anharmonic effects beyond the harmonic approximation using SSCHA, TDEP, or related methods
Generate high-quality DFT reference datasets, forces, stresses, energies, phonon dispersions, magnetic moments, that feed our MLIP training workflows.
Collaborate with our ML engineering team to design training sets that efficiently cover the relevant configuration space, and with our modelling team to provide well-converged
ab initio
parameters.
Maintain and improve our DFT workflow infrastructure, including automation, convergence protocols, and data management.
Skills & Qualifications
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 codes.
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.
Nice to Have
Experience with anharmonic methods: SSCHA, TDEP, SCAILD
Familiarity with special quasi-random structures (SQS) or cluster expansion for disordered systems.
Some background in spin-polarised DFT or magnetic materials.
Experience with automated workflow frameworks such as AiiDA or Fireworks.
Why Join Us
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're London-based with a flexible approach to how and where you work. 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.
If you're excited about this role and believe you could thrive in it, we'd encourage you to apply even if you may not align with every part of the job description.
How to Apply
If you're excited about this role and believe you could thrive in it, we'd encourage you to apply even if you may not align with every part of the job description.
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.
Hit the apply button below to submit your application. We are looking forward to hearing from you!