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Join our Seminar Series: Unlocking the future of AI in Materials Science
October 21, 2024 · Sarah Druce
Unlocking the Future of AI in Materials Science
Update - we've added a third seminar to the series! Join us this November for two cutting-edge seminars exploring the intersection of artificial intelligence, machine learning, and materials science. Orbital Materials is proud to host these exclusive events designed to showcase the potential of our newly released neural network potential, Orb. Across these seminars, we aim to promote groundbreaking work in computational chemistry and materials development and demonstrate the power of AI in advancing this field. You’ll have the opportunity to hear from world-class researchers doing research at the intersection of materials science, machine learning and computational chemistry - with some even sharing the results they’ve achieved using our Orb model.
Whether you’re an AI enthusiast, a materials scientist, or simply curious about the transformative possibilities of machine learning in this space, this seminar series is not to be missed.
Details:
Event 1: Big data and Symmetry in Machine Learning ModelsDate: November 8, 2024
Speakers:
-Marcel Langer(EPFL, Switzerland) – Testing the effects of broken symmetries in machine learning potentials -Jonathan Schmidt(ETH Zurich) – Machine Learning Discovery of Materials Marcel Langer will present his research on the effects of symmetry-breaking in non-equivariant machine learning potentials, while Jonathan Schmidt will showcase Alexandria, a new large-scale materials dataset.
Event 2: AI for Electrolyte Solution Simulations
Date: November 15, 2024
Speaker:
-Tim Duignan(University of Queensland, AUS) – Simulating electrolyte solutions with a universal neural network potential Discover how AI can solve some of the longest-standing challenges in physical chemistry. Tim Duignan will demonstrate the remarkable accuracy of the Orb model in simulating electrolyte solutions, an essential element in fields ranging from biology to chemical engineering.
Event 3: Training and Scaling Strategies for Neural Network PotentialsDate: November 21, 2024
Speakers:
-Yi-Lun Liao(MIT) -_ Equiformer and DeNS: Equivariant Transformer and Self-Supervised Learning for 3D Atomistic Systems_ -Aditi Krishnapriyan(UC Berkeley) -_ The Role of Scaling and Training Strategies for Neural Network Interatomic Potentials._ This final event in the series will explore various training and scaling strategies for neural network potentials, with Yi-Lun Liao and Aditi Krishnapriyan presenting their latest research on their findings.
Registration:
These events are free to attend, but spaces are limited. To secure your place, please register via Eventbrite:
- Event 3 Registration Stay ahead in the world of AI-driven materials science by joining us and see how Orbital Materials and our Orb model are leading the charge in the next generation of technological innovation.