The Role

We are seeking an intern for a 3-month internship to develop fast, accurate machine learning force fields (MLFFs) tailored to high-throughput Monte Carlo simulation, and integrate them into our in-house simulation framework, kUPS. You will be embedded in our chemistry team and work closely with CuspAI colleagues.

Note: You would be joining as an engineering intern within the chemistry team at CuspAI.

Your Impact

You will deliver one of the foundational capabilities our simulation stack needs to evaluate the next generation of MOFs: an MLFF that is both accurate enough to replace classical force fields for guest–host interactions and fast enough to run inside the inner loop of GCMC. By distilling state-of-the-art equivariant models into a lightweight student potential and integrating the result directly into kUPS, you will expand what is computationally tractable for CuspAI and the wider gas adsorption community.

What You Will Do

Models

• Distill MLFFs into fast student potentials optimised for Monte Carlo simulations.

• Curate, version, and document training and validation datasets, including the distillation protocol and any active-learning loops used to close coverage gaps.

Integration & Validation

• Run head-to-head validation campaigns comparing the distilled MLFF against classical force-field baselines across a curated set of guest molecules, characterising accuracy, throughput, and failure modes.

Systems & Infrastructure

• Profile and optimise the pipeline for throughput, with particular focus on the MC inner loop where MLFF inference cost dominates.

• Benchmark accuracy/speed trade-offs systematically and document where the distilled model fails.

Science & Collaboration

• Collaborate with computational chemists on reference data generation, benchmark system selection, and validation strategy.

• Contribute to a publication establishing MLFF-driven GCMC for MOF screening.

Must Have Skills and Qualifications

• Currently enrolled in (or recently completed) a PhD or Master's programme in a relevant quantitative field (Physics, Chemistry, Chemical Engineering, Computational Science, Machine Learning, or similar).

• Experience in adsorption modelling at the atomic scale.

• Hands-on experience with molecular simulation methods (GCMC, MD, or both).

• Comfortable working on Linux environments and managing simulation campaigns at scale.

• A genuine interest in the application of ML to chemistry and materials science.

Bonus Points (But Not Critical)

• Familiarity with modern MLFFs.

• Experience with knowledge distillation or other model compression techniques for scientific ML.

• Experience with active learning workflows for atomistic data.

• Familiarity with DFT data generation and the practicalities of curating atomistic datasets.

• Direct experience with established simulation packages.

• Background in gas adsorption, MOFs, or porous materials.

• Familiarity with classical force fields used in MOF simulation.

• A track record of published research at top-tier ML or computational chemistry venues.

What We Offer

• A competitive salary: We value and reward impact and growth