Lead Computational Materials Scientist — ML Potentials

Amphiform; United States; On-site; USD 120,000 - 180,000; Full time

Job summary

Amphiform seeks a highly skilled computational scientist to lead its first atomistic simulation campaign. You will predict conductivity, hydration behavior, and interfacial transport in a novel hybrid material, coordinating with experimental teams.

The role spans DFT benchmarking, amorphous structure generation, MLIP development, and large-scale MD workflows. You will work directly with the experimental team to validate predictions, advance materials/processing development, and publish the

Qualifications

• PhD in computational chemistry, physics, or materials science.
• Hands-on ab-initio MD experience on HPC.
• Trained at least one machine-learned interatomic potential (MACE, NequIP, Allegro, DeepMD, GAP) and can explain how you built the training set and verified convergence.
• Strong scientific Python

Responsibilities

• Benchmark DFT functionals against references and manage error budgets.
• Build amorphous model structures of a hybrid metal-organic network.
• Fine-tune foundation MLIPs with active learning and run DFT labeling campaigns.
• Run nanosecond-scale transport MD, metadynamics, and PIMD for diffusion and activation energies.
• Translate simulation results into materials and process development decisions; publish methodology.
• Define simulation infrastructure, data management, and reproducibility

Skills

Python

ab-initio MD

MLIPs training

HPC experience

Education

PhD in computational chemistry/physics/materials science

Tools

MACE

NequIP

Allegro

DeepMD

GAP

psiflow

Parsl

Snakemake

Job description

Amphiform seeks a highly skilled computational scientist to lead its first atomistic simulation campaign. You will predict conductivity, hydration behavior, and interfacial transport in a novel hybrid material, coordinating with experimental teams.

The role spans DFT benchmarking, amorphous structure generation, MLIP development, and large-scale MD workflows. You will work directly with the experimental team to validate predictions, advance materials/processing development, and publish the