About this webinar
Quantum-mechanical methods such as density functional theory (DFT) are accurate but limited to small systems and short timescales, while classical force fields are fast but often not accurate or transferable enough. Machine-learned interatomic potentials (MLIPs) could break this trade-off between accuracy and scale, and the field is now moving at remarkable speed. So-called universal or "foundation" models (e.g. MACE-MP, UMA, MatterSim, Orb, the DPA/OpenLAM series), pre-trained on tens to hundreds of millions of DFT calculations spanning the periodic table, approach DFT-level accuracy at a small fraction of the cost. They can be applied out-of-the-box ("zero-shot") to almost any chemistry, then fine-tuned to a specific system with a small amount of targeted data, shortening the path from question to result for both academic and industrial users.
In this webinar, we will give a conceptual tour of this rapidly evolving landscape and what it means for everyday computational research on HPC systems. We will cover:
- what universal MLIPs are and how they differ from classical force fields and system-specific ML potentials;
- the practical pre-train → fine-tune workflow and when out-of-the-box use is (and is not) good enough;
- the new generation of batched, GPU-accelerated simulation engines (e.g. TorchSim, kUPS) built for high-throughput MLIP simulation;
- how to choose and trust a model using open benchmarks;
- and close with a brief outlook on where the field is heading, including ML making its way into electronic-structure theory itself, with learned density functionals, and a short demo on European HPC resources. Throughout, we will point to open models, datasets, benchmarks, and codes that participants can try on their own problems right after the webinar.
Who is the webinar for
This webinar is intended for:
- Researchers and students in computational materials science, chemistry, and condensed-matter physics who use DFT or ab initio MD and want to reach larger systems and longer timescales without giving up accuracy
- Users of classical molecular dynamics (LAMMPS, GROMACS, ASE workflows) curious about upgrading to ML-based potentials
- Industry R&D scientists and engineers exploring AI-accelerated materials and molecular discovery
- HPC support staff and research software engineers who want an overview of the modern MLIP software stack and what it needs from GPU systems
- Anyone curious about how the foundation-model paradigm from language and vision AI is reshaping simulation in the natural sciences
No prior experience with multiplet theory, density functional theory (DFT), or many-body methods is required. Familiarity with basic concepts from solid-state or atomic physics is sufficient; the role of data and HPC in modern electronic-structure studies will be introduced at a conceptual level.
Key takeaways
By the end of this webinar, participants will:
- Understand what universal machine-learned interatomic potentials are and why they deliver near-DFT accuracy at a fraction of the cost
- Know the practical pre-train → fine-tune workflow: when an off-the-shelf foundation model is sufficient, when and how to fine-tune it with a small targeted dataset
- Get an overview of the modern MLIP software stack, from ASE/LAMMPS integrations to GPU-native, batched engines such as TorchSim, and what it takes to run it efficiently on EuroHPC systems
- Be able to critically select and validate a model using open benchmarks and understand why no single model wins on every axis
- Gain an outlook on emerging directions: long-range electrostatics, MLIPs that predict polarisation and spectra, the use of machine learning within DFT itself, and early work on AI agents that help orchestrate computational workloads
Speaker and moderator
- Karim Elgammal
- Yonglei Wang/Wei Li
For any questions contact us at training@enccs.se
More events & contact
Check out more upcoming events from ENCCS and our European network at HERE, as well as available ENCCS lesson materials, suitable also for self-learning.
For questions regarding this workshop or general questions about ENNCS training events, please contact training@enccs.se
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This training is intended for users established in the European Union or a country associated with Horizon 2020. You can read more about the countries associated with Horizon2020 HERE.