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| Plenary |
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Piero Altoe (NVIDIA, Italy)
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Revolutionizing AI-Driven Material Discovery Using NVIDIA ALCHEMI
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Evgeny Blokhin (Tilde MI & Materials Platform for Data Science, Estonia)
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Materials Platform for Data Science: A 10 Years Success Story
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Elmar Bonaccurso (Airbus Central R&T, Germany)
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Aircraft Paint System Optimization Workflow using a Combination of deterministic and data-driven Tools
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María Camarasa-Gómez (Centro de Física de Materiales CFM/MPC (CSIC-UPV/EHU), Spain)
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Optimizing DFT Hybrid Functionals for 2D Materials Using Genetic Algorithms
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Aurelie Champagne (CNRS - ICMCB, France)
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Predicting Crystal Structures and Ionic Conductivity in Mixed-Halide Solid Electrolytes Using Machine Learning Potentials
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Emigdio Chavez (Catalan Institute of Nanoscience and Nanotechnology, Spain)
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ML-driven Thermal Sensing Using FTIR Spectroscopy
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Andy Paul Chen (Nanyang Technological University, Singapore)
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Crystal Site Disorder Analysis with Machine-Learned Atomic Potentials and Statistical Methods
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Samuel John Cooper (Imperial College London, UK)
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Li-ion battery design through microstructural optimization using generative AI
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Stephen Dale (IFIM, Singapore)
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Diagonalization without Diagonalization: A Direct Optimization Approach for Solid-State Density Functional Theory
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Ignacio Fernández Graña (Pasqal, France)
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Material Discovery With Quantum-Enhanced Machine Learning Algorithms
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Amara Hakim (ONERA, France)
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Unlocking 3D Nanoparticle Shapes from 2D HRTEM images: classification and denoising at atomic resolution
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Seon-Hwa Lee (POSCO Research Institute for Future Technology, South Korea)
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Leveraging Machine Learning to Navigate Complex Design Spaces in Battery Material Development
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Sergio Lucarini (BCMaterials, Spain)
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Physics-informed neural networks for coupled Allen-Cahn and Cahn-Hilliard phase field problems
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Artem Maevskiy (National University of Singapore, Singapore)
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Machine Learning for Accelerated Discovery of Superionic Solids
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Luis Martín-Moreno (Instituto de Nanociencia y Materiales de Aragón, Spain)
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A Neural Network architecture for data-driven symmetry discovery and inverse design, with application to twistoptics
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Leonardo Medrano Sandonas (Dresden University of Technology, Germany)
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Advancing machine learning for organic material simulations with quantum accuracy
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José Luis Montaño-Priede (Centro de Física de Materiales, Spain)
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Synergistic Integration of Bayesian Optimization and Advanced Simulation Tools for Plasmonic Performance Enhancement
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Jesús Oroya (Advanced Material Simulation, Spain)
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Optimizing AI-Enhanced Neural Network Subroutines for Plasticity in FEM
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Andreas Räder (Fraunhofer Institute for Silicate Research ISC, Germany)
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OpenSemanticLab - Linked-Data-Platform with agentic AI workflows
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Jörg Schaarschmidt (Karlsruhe Institute of Technology , Germany)
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Advancing Digital Workflows in Material Science: Integrating AI into scientific workflows with the MaterialDigital Initiative
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Martin Siron (Entalpic AI, France)
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Addressing data quality issues and redundancies across chemistry databases for building better datasets for materials discovery: LeMat-Bulk
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Giovanni Vignale (IFIM, Singapore)
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Orbital-free density functional theory for periodic solids: Construction of the Pauli potential
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| 22/22 |
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| Parallel Session Seniors |
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Jose Ignacio Aizpurua (University of the Basque Country, Spain)
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Physics Informed Neural Networks for Thermal Insulation Material Ageing Estimation
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Daniel Araya Matilla (Advanced Material Simulation, Spain)
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AI-Enhanced Hybrid Modeling for Optimizing Polymeric Yarn Manufacturing Processes
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Lucas Garcia Verga (Imperial College London, UK)
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Combining DFT and Machine Learning to Enhance the Screening of Oxygen Evolution Reaction Catalysts
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Sai Gautam Gopalakrishnan (Indian Institute of Science, India)
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Optimal transfer learning strategies for predicting material properties
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Tilmann Hickel (BAM Federal Institute for Materials Research and Testing, Germany)
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Data-driven design of hydrogen solubilities in metallic alloys
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Ivan Infante (BCMaterials, Spain)
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Advancing Quantum Dot Simulations: from DFT insights to Machine Learning
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Evgeniya Kabliman (University of Bremen / Leibniz Institute for Materials Engineering – IWT, Germany)
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Symbolic regression in material science and engineering
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Clara Kirkvold (University of Birmingham, UK)
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Leveraging reticular chemistry to develop topology-informed descriptors of nanoporous materials
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Ask Hjorth Larsen (CAMD, Technical University of Denmark, Denmark)
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Automated high-throughput computational workflows with Taskblaster
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Yuting Li (Khalifa University, United Arab Emirates)
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Machine Learning Assisted Discovery of Materials for Hydrogen Energy
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Ivor Lončarić (Rudjer Boskovic Institute, Croatia (Hrvatska))
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Modeling Molecular Crystals with Machine Learning Interatomic Potentials
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Cristiano Malica (University of Bremen, Germany)
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Teaching oxidation states to neural networks
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Jose Marquez Prieto (Humboldt University of Berlin, Germany)
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NOMAD: A Distributed Platform for FAIR and AI-Ready Solar Cell Research
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Binh Duong Nguyen (Forschungszentrum Juelich GmbH, Germany)
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Machine learning for automated categorizing various defect types in KOH-etched microscopy images of 4H-SiC wafers
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Özlem Özcan Sandikcioglu (Federal Institute for Materials Research and Testing (BAM), Germany)
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Autonomous exploration of new alloy chemistries using a Material Acceleration Platform (MAP)
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Sven Rogge (Center for Molecular Modeling, Ghent University, Belgium)
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Exploring the opportunities in strain engineering: from introducing flexibility in rigid MOFs to classifying elusive amorphous states
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Jürgen Spitaler (Materials Center Leoben Forschung GmbH, Austria)
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Active learning based optimization of bainit steels based on probabilistic hybrid modeling
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Davide Tisi (EPFL, Switzerland)
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Transport mechanism of solid-state electrolytes via machine learning potentials at hybrid DFT level
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| 18/18 |
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| Parallel Session PhD Students |
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Kevin Alhada-Lahbabi (INSA Lyon, France)
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Reinforcement Learning-Assisted Ferroelectric Domain Wall Design Using a Machine Learning PhaseField Surrogate
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Adam Coxson (University of Liverpool, UK)
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Deep Learning the Fock Matrix in the Atomic Orbital Basis for extended π-conjugated molecules within a Self-Consistent Framework
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Amir Dahari (Imperial College London, UK)
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Prediction of microstructural representativity from a single image
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Pedro Julián Delgado Galindo (IFMIF-DONES España, Spain)
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Modelling of complex Fe-C systems for radiation applications with MLIAPs
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Michael Alejandro Hernandez Bertran (Istituto Nanoscienze, Consiglio Nazionale delle Ricerche CNR, Italy)
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Automated Workflows and Machine Learning models for X-ray spectra simulations: applications to Li-ion battery materials
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Onurcan Kaya (Catalan Institute of Nanoscience and Nanotechnology, Spain)
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Revealing Structure-Property Relationships in Amorphous Boron Nitride Using Machine-Learned Potentials
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Danish Khan (University of Toronto, Canada)
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Adapting hybrid density functionals with machine learning
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Ge Lei (Imperial College London, UK)
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Unveiling 3D Geometries in LLMs: The Representation and Recall of Periodic Elements
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Héctor Lobato (Leartiker, Spain)
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Smart Design of Thermoplastic Vulcanizate Products: Linking Process to Performance via Machine Learning
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Adrien Moncomble (Université Paris Cité - MPQ, France)
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aquaDenoising: AI-Enhancement of in situ Liquid Phase STEM Video for Automated Quantification of Nanoparticles Growth
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Irea Mosquera-Lois (Imperial College London, UK)
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Machine learning force fields for accurate defect calculations
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Sara Navarro (Catalan Institute of Nanoscience and Nanotechnology, Spain)
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Developing Accurate Exchange-Correlation Functionals through Physics-Informed Machine Learning
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Mojan Omidvar (Queen Mary Univeristy of London, UK)
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Accelerated Discovery of Perovskite Solid Solutions through unsupervised material fingerprints and Automated Materials Synthesis
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Sebastian Roca-Jerat (Instituto de Nanociencia y Materiales de Aragón (CSIC-Universidad de Zaragoza), Spain)
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Neural-network wave functions for quantum many-body problems
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Pol Sanz (Institute of Chemical Research of Catalonia (ICIQ), Spain)
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Optimizing Active Learning Strategies for Neural Network Potentials in Catalyst Characterization Workflows
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Lukas Volkmer (University of Technology Dresden, Germany)
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Towards a data-driven multiscale framework for quantum-mechanical investigation of elastic properties of Al-Mg-Zr alloys
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| 16/16 |
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| Lavoisier Parallel Session |
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Shubhojit Banerjee (UML, USA)
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Uncertainty-informed transferable deep learning potentials for simulating BeF2-LiF system
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Li Chen (TU Dresden, Germany)
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Towards the computational design of molecular olfactory receptors for digital odor detection
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Ganna Gryn´ova (University of Birmingham, UK)
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New Techniques for Materials Space Exploration
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Alexander Tyner (NORDITA, Sweden)
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Generative adversarial networks for inverse design of two-dimensional topological insulators
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Felipe Yamada (INESC TEC, University of Porto, Portugal)
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Enhancing Bacterial Detection by Harnessing Graphene Transistors´ Latent Features with Deep Learning
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| 4/5 |
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| 60/61 |
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