Computational Materials Scientist for Machine-Learned Interatomic Potentials (MD/DFT)

About Amphiform:

Amphiform ( ) is building next-gen energy materials for AI data centres, defence and space: light, abundant and powerful. We achieve that by creating a new type of matter: hybrid materials, built atomic layer by atomic layer, where every atom has a pre-programmed purpose.

We just closed a $5.5+1m pre-seed led by General Catalyst and Main Object, with Thomas Wolf (Hugging Face), Charlie Songhurst, and others.

Do you have the following skills, experience and drive to succeed in this role Find out below.

The role:

We are developing a new class of material: an ultrathin, vapour-deposited hybrid materials with custom properties - and we want to understand the fundamental properties. You will own our first atomistic simulation campaign: predicting conductivity (electron, ion, heat), hydration behaviour, and interfacial transport in a material that has never been simulated, and testing your predictions against experiments that 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 will 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: xhyhwjd

• PhD in computational chemistry, physics, or materials science
• Hands-on ab-initio MD experience on HPC
• You have 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