🚀 Mission Highlights

As a Computational Chemist Intern at Entalpic, you will work at the intersection of quantum chemistry, machine learning, and atomic-scale engineering. Your role centres on exploring surface chemistry using high-throughput DFT workflows and Machine Learning methods — investigating reaction pathways, molecular dynamics, and transition states — potentially combined with multi-scale approaches to bridge atomic-scale simulations with process-level behaviour.

You will contribute directly to Entalpic's core discovery pipeline within our science & engineering team (~25 persons), tackling real R&D challenges in atomic-scale manufacturing processes relevant to semiconductors, batteries, photovoltaics, and beyond.

✨ Role & Responsibilities

This position directly supports the company's mission of discovering materials and processes to optimize carbon-intensive industries. You will be responsible for:

• High-throughput DFT workflows — Design, run, and automate quantum mechanical simulations (surface adsorption, reaction pathways, transition states) using tools such as ASE, VASP, or CP2K, contributing to systematic, large-scale datasets.
• Surface & molecular modelling — Investigate surface reaction mechanisms, adsorption energies, and transition state geometries relevant to atomic layer processes.
• Multi-scale modelling — Bridge atomic-scale simulations with mesoscale process behaviour using Molecular Dynamics, Kinetic Monte Carlo (KMC), and QM/MM methods.
• Workflow agentification — Contribute to automating and orchestrating DFT pipelines within Entalpic's active learning framework, reducing human-in-the-loop bottlenecks and enabling faster iteration cycles. Automate the configuration of DFT parameters.
• ML model application & fine-tuning — Apply and fine-tune existing ML models (MLIPs, property predictors) on DFT-generated and experimental data; contribute to model validation and benchmarking against quantum mechanical references.

🤓 Expertise & Skills

• PhD student (preferred) or Master's student with substantial research experience in computational chemistry, materials science, chemical physics, or a closely related field.
• Strong background in quantum mechanics and DFT — hands-on experience with base simulation packages (VASP, Quantum ESPRESSO, CP2K, Orca, LAMMPS or equivalent).
• Experience with high-throughput computational workflows — familiarity with workflow managers (e.g. Atomate, JobFlow, AirFlow, AiiDA, Fireworks, ASE workflows) or scripting-based automation of DFT calculations.
• Familiarity with ML models applied to atomistic systems — knowledge of MLIPs (e.g. MACE, UMA, CHGNet) or molecular property prediction models is a strong asset; experience with fine-tuning or transfer learning on existing models is a plus.
• Proficiency in Python, Pytorch, Slurm (supercomputers) and version control (Git).
• Strong analytical skills, scientific rigour, and ability to work in a fast-paced startup environment.
• Excellent communication skills in English.