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Software engineer
- Location
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- Type
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- Compensation
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- Posted
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Computational Materials Scientist: Machine-Learned Interatomic Potentials (MD/DFT)
- Type
- Full time
- Compensation
- Salary: Flexible · Equity: 0.3-0.6%
- Posted
- 18D AGO
About the role
Computational Materials Scientist: Machine-Learned Interatomic Potentials (MD/DFT)
Salary
Flexible
Equity
0.3-0.6%
Location
Flexible
Type
Full-time
About the role
You’ll own our first atomistic simulation campaign on an ultrathin, vapour-deposited hybrid material that has never been simulated: predicting conductivity (electron, ion, heat), hydration behaviour and interfacial transport, and testing your predictions against experiments we’ll run constantly alongside. The workflow (DFT benchmarking → amorphous structure generation → fine-tuned machine-learned interatomic potentials with active learning → large-scale transport MD, enhanced sampling and path-integral MD) is scoped, budgeted and precedented in the recent literature; the material is not. You’ll be our first dedicated computational hire, working directly with the experimental team.
What you’ll do
- Benchmark DFT functionals against coupled-cluster references for proton-transfer energetics, and own the resulting error budget
- Build amorphous model structures of a hybrid metal-organic network, validated against our data
- Fine-tune foundation MLIPs (MACE-class) with active learning; run DFT labelling campaigns
- Run nanosecond-scale transport MD, metadynamics and PIMD to extract diffusion coefficients, activation energies, isotope effects and interfacial behaviour
- Turn simulation into decisions: feed results into our materials and process development, and publish the methodology with us
- Define our simulation infrastructure: environments, data management, reproducibility
What we’re looking for
- PhD in computational chemistry, physics or materials science
- Hands-on ab-initio MD experience on HPC
- You’ve trained at least one machine-learned interatomic potential (MACE, NequIP, Allegro, DeepMD, GAP or similar) and can explain how you built the training set and knew it was converged
- Strong scientific Python
Nice to have
- Ion transport background
- Amorphous/disordered structure generation
- Path-integral MD (i-PI), GCMC (RASPA), charge-partitioning workflows, or workflow engines (psiflow, Parsl, Snakemake)
- A benchmark culture: DLPNO-CCSD(T), functional selection, error bars you can defend
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