BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//ENCCS - ECPv6.17.2//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:ENCCS
X-ORIGINAL-URL:https://enccs.se
X-WR-CALDESC:Events for ENCCS
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:Europe/Stockholm
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20250330T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20251026T010000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20260329T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20261025T010000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20270328T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20271031T010000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Europe/Stockholm:20260930T100000
DTEND;TZID=Europe/Stockholm:20260930T113000
DTSTAMP:20260702T143218Z
CREATED:20260702T143218Z
LAST-MODIFIED:20260702T143218Z
UID:38711-1790762400-1790767800@enccs.se
SUMMARY:[Webinar] Foundation Models for Atoms: Machine-Learned Interatomic Potentials in Practice
DESCRIPTION:About this webinar\nQuantum-mechanical methods such as density functional theory (DFT) are accurate but limited to small systems and short timescales. 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) are pre-trained on tens to hundreds of millions of DFT calculations spanning the periodic table. These models 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. Afterward\, they can be fine-tuned to a specific system with a small amount of targeted data. This shortens the path from question to result for both academic and industrial users. \nIn 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: \n\nwhat universal MLIPs are and how they differ from classical force fields and system-specific ML potentials;\nthe practical pre-train → fine-tune workflow and when out-of-the-box use is (and is not) good enough;\nthe new generation of batched\, GPU-accelerated simulation engines (e.g. TorchSim\, kUPS) built for high-throughput MLIP simulation;\nhow to choose and trust a model using open benchmarks;\nand close with a brief outlook on where the field is heading. This includes ML making its way into electronic-structure theory itself\, with learned density functionals. We will also include a short demo on European HPC resources. Throughout\, we will point to open models\, datasets\, benchmarks\, and codes. Participants can try these on their own problems right after the webinar.\n\nWho is the webinar for\nThis webinar is intended for: \n\nResearchers 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\nUsers of classical molecular dynamics (LAMMPS\, GROMACS\, ASE workflows) curious about upgrading to ML-based potentials\nIndustry R&D scientists and engineers exploring AI-accelerated materials and molecular discovery\nHPC support staff and research software engineers who want an overview of the modern MLIP software stack and what it needs from GPU systems\nAnyone curious about how the foundation-model paradigm from language and vision AI is reshaping simulation in the natural sciences\n\nNo 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. \nKey takeaways\nBy the end of this webinar\, participants will: \n\nUnderstand what universal machine-learned interatomic potentials are and why they deliver near-DFT accuracy at a fraction of the cost\nKnow 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\nGet 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\nBe able to critically select and validate a model using open benchmarks and understand why no single model wins on every axis\nGain 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\n\nSpeaker and moderator\n\nKarim Elgammal\nYonglei Wang/Wei Li\n\nFor any questions contact us at training@enccs.se \nMore events & contact\nCheck out more upcoming events from ENCCS and our European network at HERE\, as well as available ENCCS lesson materials\, suitable also for self-learning. \nFor questions regarding this workshop or general questions about ENNCS training events\, please contact training@enccs.se \nSchedules can change!\nTo ensure that everyone has the opportunity to participate\, we kindly request that you let us know as soon as possible if you are unable to attend an event after registering. \nPlease send us an email at training@enccs.se to cancel your attendance. \nWe understand things can change\, but repeated cancellations without notice may unfortunately result in your name being removed from future event registration lists. \nRegulations\nDue to EuroCC2 regulations\, we CAN NOT ACCEPT generic or private email addresses. Please use your official university or company email address for registration. \nThis 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.
URL:https://enccs.se/event/webinar-foundation-models-for-atoms-machine-learned-interatomic-potentials-in-practice/
LOCATION:Online
ATTACH;FMTTYPE=image/jpeg:https://media.enccs.se/2026/07/foundation-models-1.jpg
END:VEVENT
END:VCALENDAR