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| Plenary |
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Penghui Cao (University of California, Irvine, USA)
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Neural network kinetics: exploring diffusion multiplicity and chemical ordering in compositionally complex materials
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Claudio Cazorla (ICREA (Institut Català de Recerca i Estudis Avançats), Spain)
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Uncertainty-Aware Machine Learning Discovery of Solid–Solid Phase Transitions in Inorganic Materials
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Simon Delacroix (Ecole Polytechnique , France)
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High-throughput laser synthesis and active learning for optimization of luminescent materials
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Dorye L. Esteras (ICN2, Spain)
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Modelling and simulation of magnetic materials via AI-driven workflows
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Burak Gurlek (Max Planck Institute for the Structure and Dynamics of Matter, Germany)
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Transferable Machine-Learned Potentials for Vibrational Dynamics from Acene Crystals to Single-Molecule Host–Guest Systems
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Javier Heras Domingo (Universitat de Barcelona, Spain)
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Multi-Modal Artificial Intelligence for Molecular Structure Identification using Infrared and Raman Spectroscopy
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Elisabeth Keller (Technical University of Denmark, Denmark)
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Learning G0W0 Corrections in Real Space with Equivariant Neural Networks
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Christopher Kuenneth (University of Bayreuth, Germany)
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The Polymer Chemical Linguist: polyBERT´s Role in Next-Generation Polymer Informatics
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Jolla Kullgren (Chemistry - Ångström, Uppsala University, Sweden)
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Predictions and/or insight? - ML and physics-based NMR and IR spectroscopy for water in, and on, crystals
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Joseph Musielewicz (Entalpic, France)
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TriForces: Augmenting Atomistic GNNs for Transferable Representations
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Vincenzo Palermo (CNR, Italy)
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Artificial Neural Network–Assisted Electrochemical Sensors for Reliable Biomarker Analysis in Complex Fluids
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Laura-Bianca Pasca (University of Oxford, UK)
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Digital experiments for molecular passivation of hybrid perovskite surfaces
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Irina Roslyakova (GTT-Technologies, Germany)
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High-Throughput Materials Informatics Integrating Ab Initio, Machine Learning and CALPHAD Data
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Andrew Salij (Los Alamos National Laboratory, USA)
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Multi-objective Materials Discovery using Weighted Preference Optimization
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Kasper Tolborg (Aalborg University, Denmark)
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Modelling the interplay between vibrations and disorder in crystalline materials
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Libor Vojacek (Paul Scherrer Institute PSI, Switzerland)
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Polarons and charge-transfer excitations from grand-canonical neural networks
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Valentyn Volkov (XPANCEO, United Arab Emirates)
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Data-driven discovery of novel materials for smart electronic devices
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Ge Wang (University of Science and Technology Beijing, China)
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LLM-Enabled MOFs Discovery: Bridging Rational Design and Laboratory Realization
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Xinwei Wang (Imperial College London, UK)
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Multi-fidelity machine learning interatomic potentials for charged point defects
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Julija Zavadlav (Technical University of Munich, Germany)
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Machine Learning Potentials with Experimental Data in the Loop
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| 18/20 |
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| Parallel Session Seniors |
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Hakim Amara (LEM/ONERA-CNRS, France)
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Deep Learning-Based Automatic Classification of Nanoparticle Morphologies: Leveraging Synthetic Data for Experimental Characterization
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Abril Azocar Guzman (Forschungszentrum Julich, Germany)
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Knowledge Graphs for Data-Driven Computational Materials Research
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Rohit Batra (IIT Madras, India)
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Automated Extraction of Multicomponent Alloy Data Using Large Language Models for Sustainable Design
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Natalia Bedoya (Materials Center Leoben Forschung GmbH (MCL), Austria)
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ALPmat: A Platform for Collaborative AI-driven Advanced Materials Design
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Albert Bruix (Universitat de Barcelona, Spain)
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Accelerating the structural and chemical characterization of nanostructured materials under reaction conditions with ML-guided Grand Canonical Global Optimization
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Luiz Felipe Cavalcanti Pereira (Universidade Federal de Pernambuco , Brazil)
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Machine learning-aided search of enhanced elastocaloric effect in graphene kirigami
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Emigdio Chavez Angel (Catalan Institute of Nanoscience and Nanotechnology, Spain)
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Machine Learning-Assisted Detection of Water Contaminants Using Conventional Raman Spectroscopy
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Raffaele Cheula (Aarhus University, Denmark)
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Graph models and fine-tuned machine learning potentials for microkinetic analyses in heterogeneous catalysis
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Bousige Colin (Lab. of Multimaterials and Interfaces, Univ. Lyon1 / CNRS, France)
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Understanding the nucleation and growth of borophene on substrate using Machine Learning Tools
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Pierre-Paul De Breuck (Ruhr University Bochum, Germany)
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A generative material transformer using Wyckoff representation
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Antonino Famulari (Politecnico di Milano, Italy)
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Hybrid Classical-Quantum Computing approaches to the study of the electronic structure of materials.
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Pau Ferri Vicedo (Instituto de Tecnologia Quimica, Spain)
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High-Throughput Transition-State Searches in Zeolite Nanopores with NNPs
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Eider Garate Perez (Tekniker, Spain)
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Machine Learning Surrogates for Phase-Field Modeling of Dendritic Metal Solidification
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Andrey Golov (CIC Energigune, Spain)
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Machine-Learning Interatomic Potentials for the Investigation of Solid Electrolyte Interphase Formation
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Carlos Gonzalez (IMDEA MATERIALS / UPM, Spain)
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Toward Intelligent Digital Twins for Liquid Composite Moulding
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Sai Gautam Gopalakrishnan (Indian Institute of Science, India)
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Predicting ionic motion in solids using transfer learning
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Sergio Gutiérrez Rodrigo (Universidad de Zaragoza; Instituto de Nanociencia y Materiales de Aragón (INMA), Spain)
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Physics-Informed Neural Networks in Materials Science: a framework for optimization, symmetry identification, and inverse design
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Annica Heyne (Federal Institute for Materials Research and Testing (BAM), Germany)
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Automated Optimization of the Electrodeposition of Alloy Thin Films using a Material Acceleration Platform (MAP)
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Malcolm Jardine (Universitat de Barcelona, Spain)
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Leveraging Supervised Machine Learning to Predict Band Gaps of Modular Materials from Their Molecular Building-Blocks
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Onurcan Kaya (ICN2, Spain)
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Revealing Structure-Property Relationships in Amorphous Boron Nitride Using Machine-Learned Potentials
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Ivan Kruglov (Emerging Technologies Research Center, XPANCEO, United Arab Emirates)
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OptiXNet: Symmetry-Aware Equivariant Network for Discovering SHG-Active Materials
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Rachid Laref (laboratoire UCCS, Université d´Artois , France)
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Accelerated Dimensionality Prediction of Lead Halide Perovskites via Wavelet Convolutional Neural Networks
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Zhenzhu Li (Imperial Global Singapore, Singapore)
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Platonic representation of foundation machine learning interatomic potentials
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Alexander Lobo (BCG X AI Science Institute, USA)
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Hybrid AI–Physics Discovery of Ionic Liquids Under Industrial Carbon Capture Constraints
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Daniel Marchand (SINTEF, Norway)
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Evolutionary Coding Agents for Autonomous Optimization of Scientific Software and Metallurgical Design
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Francisco Martin-Martinez (King´s College London, UK)
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Embedded molecular representations for more efficient machine learning in molecular discovery and chemical property prediction
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David Mercier (Ansys Inc. Part of Synopsys, France)
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Unsupervised Spatial Machine Learning for Phase Clustering in Nanomechanical Maps with Kernel-Averaged Mechanical Mismatch
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Pierre Mignon (Université Lyon1 - institut Lumière Matière, France)
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Simulation of STM Surface Images from 3D Atomic Structures. A Unet-based Convolutional Networks Tool.
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Ehsan Moradpur Tari (University of Tartu Institute of Technology, Estonia)
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A correlation-based optimization model to recover lost and distorted data from scanning tunneling microscopy images based on density functional theory
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Santiago Muiños Landin (AIMEN Techology Centre, Spain)
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Generative AI for Materials Discovery and SSbD-Driven Material Selection: From Inverse Design to Knowledge Extraction for Faster Validation
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Joaquín Muñoz Rodríguez (Bird & Bird LLP, Spain)
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Data Protection and Trade Secrets in AI-Powered Materials Databases: An Integrated Legal Framework
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David Nieto Simavilla (ETSIME-UPM, Spain)
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GINNs: A GENERIC Informed Neural Networks methodology to learn thermodynamically sound rheological models
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Nikita Orekhov (XPANCEO, United Arab Emirates)
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Molecular dynamics with machine-learning potentials for describing defect dynamics in graphene and diamond
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Lukas Powalla (Robert Bosch GmbH, Germany)
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Ontology Extraction for Electric Drive Materials Using AI Agents
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Christina Schenk (IMDEA Materials Institute, Spain)
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Bayesian Calibration with Optimized Surrogate Models for Materials and Engineering
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Simon Stier (Fraunhofer ISC, Germany)
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Towards Self-Organizing Research Data: Multi-Agent AI for Autonomous Knowledge Graph Operations based on Object-Oriented Linked Data
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Abigail Teitgen (Instituto de Ciencia de Materiales de Madrid (ICMM-CSIC), Spain)
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A Multitask Graph Neural Network Framework for Ames Mutagenicity Prediction
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Emanuele Telari (Universitat de Barcelona, Spain)
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Charting nanocluster structures via convolutional neural networks
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Richard Tran (Entalpic, France)
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Exploring dopant effects on cathode synthesizeability and voltage stability with high-throughput ML
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Andrey Ustyuzhanin (Constructor University, Germany)
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What Spin Glasses Teach Us About AI Architecture
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Sergi Vela (Institut de Química Avançada de Catalunya (IQAC-CSIC), Spain)
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AI-Driven Molecular Discovery through Automated Dataset Generation and Execution
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| 37/41 |
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| Parallel Session PhD Students |
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Rayen Ben Ismail (University of Nottingham, UK)
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Towards Scalable Gallium Selenide Epitaxy on Graphene: A Multiscale DFT-KMC Framework for Optimizing Growth Conditions
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Jonas Böhm (ICMCB-CNRS, France)
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Predicting Crystal Structures and Ionic Conductivities in Li3YCl6−xBrx Halide Solid Electrolytes Using a Fine-Tuned Machine Learning Interatomic Potential
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Cyprien Bone (University College London, UK)
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Discovery and recovery of crystalline materials with property-conditioned transformers
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Joana Cecibel Bustamante Pineda (Federal Institute for Materials Research and Testing (BAM), Germany)
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Lattice thermal conductivity on argyrodite compounds Ag8TS6 (T= Si, Ge and Sn): Experimental and Theoretical approach
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Junwu Chen (EPFL, Switzerland)
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Generative Artificial Intelligence for Inverse Materials Design
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Mirko Fischer (University of Münster / Institute for Physical Chemistry, Germany)
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From Oligomers to entangled Polymers: How transferable are Machine Learning Interatomic Potentials?
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Mathilde Franckel (Imperial College London, UK)
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LeMat-Rho: High-Fidelity Charge Density Dataset for Machine Learning and Atomistic Materials Modeling
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Manuel González Lastre (Universidad Autónoma de Madrid, Spain)
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MAD-SURF: a general machine-learning interatomic potential for molecular adsorption on metal surfaces
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Stefaan Hessmann (TU Berlin, Germany)
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Generative Pseudo-Force Fields for Structure Generation
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Pranav Kakhandiki (Stanford University, USA)
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Efficient Nudged Elastic Band Method using Neural Network Bayesian Algorithm Execution
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Luke Keenan (Trinity College DUblin, Ireland)
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Machine Learning Accelerators for Quantum Transport
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Ge Lei (Imperial College London, UK)
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From Prompt to Protocol: Fast Charging Batteries with Large Language Models
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Aakash Ashok Naik (Federal Institute for Materials Research and Testing (Bundesanstalt für Materialforschung und -prüfung), Germany)
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Machine Learning driven insight into Bonding Heterogeneity Effects on Thermal Conductivity
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Mert Ozan (Federal Institute for Materials Research and Testing(BAM), Germany)
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Machine learning aided, closed-loop optimization of electrodeposition processes in a Material Acceleration Platform
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Efe Mehmet Peker (Bundesanstalt für Materialforschung und-prüfung (BAM), Germany)
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Balance between precision and scalability: Kinetic Monte Carlo Simulation of Electrodeposition Processes
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Sara Shahbazi Fashtali (Sapienza Università di Roma, Italy)
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Fine-Tuned Ab Initio–Trained MACE Model for Predictive Mechanical Modeling of Graphene Oxide
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Jorge Suárez Recio (Universidad Politécnica de Madrid, 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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Viktor Svahn (Uppsala university, Sweden)
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Limitations of cluster-trained MLIPs for liquid density and diffusivity
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Aleksander Szewczyk (TU Dresden, Germany)
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Data-Driven Exploration of Thermal and Elastic Properties in Covalent Organic Frameworks
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Mary Tabut (Sorbonne University, France)
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ML-SAPIE: An Autonomous Workflow Bridging High-Throughput DFT and Machine Learning for Surface Interface Discovery
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Alex Teruel (Basque Center for Applied Mathematics, Spain)
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Screening Energetically Stable Structures in Solid-State Ionics Applications
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Sheares Toh (Imperial College London, UK)
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MAESTRO: An AI agent orchestrator for battery materials discovery
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Elohan Veillon (Université d´Artois, France)
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Fourier Transformers for Latent Crystallographic Diffusion and Generative Modeling
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Haolin Wang (University of Sheffield, UK)
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Benchmarking Bandgap Prediction in Semiconductors under Experimental and Realistic Evaluation Settings
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Yunyu Zhang (University College London, UK)
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A Multi-Scale Mixture of Experts Model for Structural Prediction of Cu Nanoparticles
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| 23/25 |
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| 78/86 |
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