Trusted and reproducible workflows for machine learnt interatomic potentials - PSDI webinar скачать в хорошем качестве

Trusted and reproducible workflows for machine learnt interatomic potentials - PSDI webinar

The Physical Sciences Data Infrastructure (PSDI) aims to accelerate research in the physical sciences by providing a data infrastructure that brings together and builds upon the various data systems researchers currently use. Our webinar series provide updates on our exploratory pathfinder work, and on relevant tools and technologies that have been developed by members of our community. This video presents a recording of the "Trusted and reproducible workflows for machine learnt interatomic potentials" webinar presented by Elliott Kasoar and Dr Alin-Marin Elena on 8th January 2026. https://www.psdi.ac.uk/event/webinar-... Abstract: This webinar explores recent advances in machine learnt interatomic potentials (MLIPs) that revolutionize atomistic simulations with ab initio accuracy and expanded scales, while introducing software frameworks such as janus-core, aiida-mlip, and ML-PEG for data generation, benchmarking, training, and workflow integration within the PSDI ecosystem. 00:00 Introduction 01:30 Presentation 41:44 Questions 42:42 Outro