Internship - MLFF Distillation & GCMC Integration

Develop fast, accurate machine-learning force fields (MLFFs) for high-throughput Monte Carlo and integrate them into the company’s in-house simulation framework, kUPS. Distill state-of-the-art equivariant models into lightweight student potentials, curate and document training/validation datasets, and run head-to-head validation versus classical force-field baselines. Profile and optimize the MC inner loop for throughput and contribute to research advancing MLFF-driven GCMC for MOF screening.