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Piero Altoe (NVIDIA, Italy)
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Accelerating the Discovery Loop: Production-Ready Tools for GPU-Powered Materials Science
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Elmar Bonaccurso (Airbus, France)
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Machine Learning for Optimized E2E Advanced Materials Lifecycle - Materials Design, Development, Analytics & Testing
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Keith Butler (University College London, UK)
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Learning the language of crystal chemistry: How methods from natural language can help discover new materials
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Maria K. Chan (Argonne National Laboratory, USA)
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MaterialEyes — Seeing the Invisible using Experiment, Theory, and AI
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Jacqueline Cole (University of Cambridge, UK)
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Data-driven Materials Science for Energy-Sustainable Applications
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Gabor Csanyi (University of Cambridge, UK)
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Machine learned force fields: status and challenges in 2026
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Maurice de Koning (Instituto de Física Gleb Wataghin, Brazil)
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Deformation-trained Deep Potential model for superionic water ice: transferability from stress-strain data to phase equilibria
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Andrés Díaz Lantada (UPM/IMDEA Materiales, Spain)
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AI-enhanced design for additive manufacturing of bioinspired, smart and living materials and devices
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Roberto Luis Iglesias Pastrana (Universidad de Oviedo, Spain)
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Helium Effect on Self-Healing at Tungsten Grain Boundaries Using a DFT-Based Machine Learning Interatomic Potential
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Saeed Jahromi (Multiverse Computing & DIPC, Spain)
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Quantum-Inspired Tensor Networks for Materials Simulation and AI Applications
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Yousung Jung (Seoul National University, South Korea)
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AI for Synthesizable Materials Discovery: From Prediction to Autonomous Design
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Miguel Marques (Ruhr Universitat Bochum, Germany)
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Machine-learning materials science
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Reinhard Maurer (University of Warwick, UK)
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Machine Learning Surrogate Models
of Electronic Structure
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Rampi Ramprasad (Georgia Tech, USA)
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AI-assisted design of functional polymers for a sustainable world
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Trevor David Rhone (Rensselaer Polytechnic Institute, USA)
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Accelerating the discovery of van der Waals quantum materials using AI
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Milica Todorovic (University of Turku, Finland)
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Advances in active learning for materials optimisation
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Tejs Vegge (Technical University of Denmark, Denmark)
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MaterialsCommons and FAIR workflows for federated discovery of advanced materials
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