🚀 Mission Highlights

As a Computational Chemist 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 DFT and ML methods — investigating reaction pathways, molecular dynamics, and transition states — combined with multi-scale approaches to bridge atomic-scale simulations with process-level behaviour.

You will be a key contributor to Entalpic's core discovery pipeline, owning and advancing our atomistic modelling capabilities across real R&D challenges in atomic-scale manufacturing processes (ALD, ALE, CVD) 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:

• Surface & molecular modelling — Lead investigations into surface reaction mechanisms, adsorption energies, and transition state geometries relevant to atomic layer processes (metal organic complexes), using DFT and semi-empirical methods as primary tools.
• 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.
• Multi-scale modelling — Bridge atomic-scale simulations with mesoscale process behaviour using Molecular Dynamics, kinetic Monte Carlo (kMC), meta-dynamics, and QM/MM methods; develop and integrate multi-scale workflows into Entalpic's discovery pipeline.
• ML model application & fine-tuning — Apply and fine-tune existing ML models (MACE, UMA, etc.) on DFT-generated and experimental data; contribute to model validation and benchmarking against quantum mechanical references.
• Workflow agentification — Drive the automation and orchestration of DFT pipelines within Entalpic's active learning framework, reducing human-in-the-loop bottlenecks and enabling faster iteration cycles.
• Scientific leadership — Contribute to publications, patents, and client-facing deliverables; mentor junior team members and interns; engage with industrial and academic partners to ensure computational discoveries are experimentally grounded.

🤓 Expertise & Skills

• PhD in Computational Chemistry, Materials Science, Chemical Physics, or a closely related field, with 2+ years of industry experience.
• Deep expertise in quantum mechanics and DFT — extensive hands-on experience with simulation packages (VASP, CP2K, Orca, LAMMPS, or equivalent) and a strong understanding of the underlying physics.
• Proven track record in high-throughput computational workflows — experience designing, running, and maintaining large-scale DFT campaigns using workflow managers (e.g. Atomate, Jobflow, Fireworks, ASE workflows).
• Strong experience with ML models applied to atomistic systems — knowledge of MLIPs or molecular property prediction models; experience with fine-tuning, transfer learning, or active learning workflows is a strong asset.
• Multi-scale modelling experience — familiarity with at least one of kMC, QM/MM, or related mesoscale methods is a significant plus.
• Proficiency in Python, PyTorch, Slurm, and version control (Git).