This webinar continues our look at our pathfinder activities in the current phase. The subject of this webinar is pathfinder 7 and will discuss how their software helps in workflows containing large data sets generated by simulations and experiments at national facilities. This webinar will be presented by Leandro Liborio from STFC, Harwell

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Register here: https://us06web.zoom.us/webinar/register/WN_7t0s4FqJSYiL8e0UoHqeYA This webinar continues the PSDI webinar series, in this webinar representatives from PSDI Partner the Henry Royce Institute present their Ampletracks sample tracking system.This webinar will be presented by Stavrina Dimosthenous (Henry Royce Institute) and Ben Jefferson. Abstract  Ampletracks is a configurable web-app built, originally, for sample tracking in and out of laboratory environments. In this talk, we will discuss Ampletracks in brief, and why and how we built Ampletracks. We will demonstrate Ampletracks implemented for a materials science audience. We will demonstrate the ‘User’ workflow, and Ampletracks features across the multiple ways to register a sample record, and intrinsic (like record type) and extrinsic (unlike record type) relationships between records. We will then demonstrate how to set different permission profiles for users, and ‘superuser’ permissions. To finish, we will briefly discuss other domains to apply Ampletracks, and how we use Ampletracks to track the development of Ampletracks.Stavrina Dimosthenous is a Data Curator at the Henry Royce Institute for advanced materials (Royce) in the UK, based at the Royce Hub Building at The University of Manchester. She holds a PhD in Materials Science, and has conducted research in materials science and computational materials science across the UK […]

Register here: https://us06web.zoom.us/webinar/register/WN_jk6RwUt7SbuHz4toJm31zQ This webinar continues our look at our pathfinder activities in the current phase. The subject of this webinar is pathfinder 5 which focuses on transforming data to knowledge through the construction of workflows. In particular this looks machine learning for interatomic potentials (MLIP).This webinar will be presented by Alin Elena and Federica Zanca from Science and Technology Facilities Council Daresbury Laboratory. Abstract  AI is ubiquitous in all walks of life and research. In the last 5 years active research in the field of understanding how atoms and molecules interact using AI has revolutionised the field. The outcome of these efforts, machine learning interatomic potentials, MLIP, has produced the breakthrough that recommends them as the next paradigm change in atomistic molecular simulations, with applications ranging from battery design to catalytic chemical reaction modelling for hydrogen storage or CO2 capture. All these advances need expensive calculations to be produced and used for training machine learning models. In addition, the models resulting from these models are non-trivial in terms of storage and distribution compared with previous generation interatomic potentials which tended to be analytical. Current models use millions of structures for training, and this raises new challenges around reproducibility and […]