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Wed 03 |
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Thu 04 |
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08:45-09:00 |
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Opening Ceremony |
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Chairperson: Stephan Roche |
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09:00-09:40 |
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PLENARY |
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Innovative material design |
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Kostya Novoselov,
NUS, Singapore |
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09:40-10:10 |
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KEYNOTE |
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Physics based machine learning for materials and compound space |
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Anatole von Lilienfeld,
University of Toronto, Canada |
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10:10-11:10 |
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Coffee Break / Poster Session / Exhibition |
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Chairperson: Anatole von Lilienfeld |
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11:10-11:30 |
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INVITED |
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Machine Learning for Molecular Sensing |
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Gianaurelio Cuniberti,
TU Dresden, Germany |
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11:30-11:45 |
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Multivariate sensing of sodium and potassium ions using Prussian blue, graphene oxide electrodes and machine learning |
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Vincenzo Palermo,
CNR-ISOF, Italy |
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11:45-12:00 |
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Understanding Domain Reconstruction of Twisted Bilayer and Heterobilayer Transition Metal Dichalcogenides through Machine Learned Interatomic Potentials |
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Nicholas Hine,
University of Warwick, UK |
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12:00-12:15 |
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Adaptive AI-Driven Material Synthesis: Towards Autonomous 2D Materials Growth |
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Antonio Rossi,
Istituto Italiano di Tecnologia, Italy |
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12:15-12:30 |
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Machine Learning for Nanoparticle Synthesis |
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Marek Grzelczak,
Centro de Fisica de Materiales (CSIC-UPV/EHU), Spain |
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12:30-13:00 |
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KEYNOTE |
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Some intersections of photonics and AI |
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Marin Soljacic,
MIT, USA |
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13:00-14:00 |
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Cocktail Lunch (offered by AI4AM2024 organisers) |
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14:00-14:30 |
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Poster Session 1 / Exhibition |
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Chairperson: Gianaurelio Cuniberti |
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14:30-15:00 |
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KEYNOTE |
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Machine Learning for autonomous microscopy: from physics discovery to atomic fabrication |
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Sergei V. Kalinin,
UT Knoxville and Pacific Northwest National Laboratory, USA |
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15:00-15:15 |
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Refining Molecular Characterization to allow machine learning of the effectiveness of corrosion inhibitors |
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Ivan Cole,
RMIT University, Australia |
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15:15-15:35 |
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INVITED |
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Variational autoencoders-enabled high-fidelity reconstruction and effective anomaly detection in EEG data |
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Giulia Cisotto,
University of Milan-Bicocca, Italy |
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15:35-16:15 |
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Coffee Break / Poster Session |
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Chairperson: Giulia Cisotto |
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16:15-17:15 |
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Constructor sponsored Session
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AI Trends in Advanced Materials, Nick Dobrovolskiy, Constructor Technology
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Constructor Platform and Knowledge Models, Egor Alekseev, Constructor Technology
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Foundation models for material science, Andrey Ustyuzhanin, National University of Singapore
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Sparse representation for machine learning the properties of defects in 2D materials, Kostya Novoselov, Nikita Kazeev, Institute for Functional Intelligent Materials, NUS
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AI-powered prediction of materials, Stephan Roche, Andrei Tomut, ICN2
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17:15-17:35 |
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INVITED |
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Predicting material properties with the help of machine learning |
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Bingqing Cheng,
UC Berkeley, USA |
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17:35-17:50 |
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Unlocking the Potential of EXAFS: Machine Learning Approaches for Spectroscopic Data |
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Javier Heras-Domingo,
ICIQ, Spain |
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20:30 |
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Conference Dinner - CAN TRAVI NOU
Final C/ Jorge Manrique s/n (Barcelona)
Nearest metro stop (12 minutes walking) Horta - Line 5
Google Maps - More info |
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