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Posters (57) - Alphabetical order |
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| Poster nº |
Author & Title |
Abstract |
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| 43 |
Zain Ul Abideen (BCMaterials, Spain) |
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| Implementation of a Machine Learning Force Fields Platform for Quantum Dots
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| 10 |
Sepideh Baghaee Ravari (Interdisciplinary Centre For Advanced Materials Simulation,Ruhr-Universität Bochum, Germany) |
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| FAIR Semantic-Driven Analysis of Defect Properties in Metals
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| 49 |
Mohammed Benaissa (Institut de Physique de Rennes, France) |
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| Converging the Infeasible: Machine Learning and Renormalization in Multiple scattering Simulations
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| 2 |
Diogo Cachetas (International Iberian Nanotechnology Laboratory, Portugal) |
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| Enhancing Antibiotics Detection in Raman Spectra with Deep Generative Models
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| 51 |
Pablo Calvo-Barlés (Instituto de Nanociencia y Materiales de Aragón, Spain) |
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| Learning finite symmetry groups of dynamical systems via equivariance detection
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| 4 |
Lukas Cvitkovich (University of Regensburg, Germany) |
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| Harnessing Artificial Intelligence for Predicting Proximity Effects in Van der Waals Heterostructures
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| 32 |
Lucas Thiago Siqueira de Miranda (IFT - UNESP, Brazil) |
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| Hexagonal ice density dependence on inter atomic distance changes due to nuclear quantum effects
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| 21 |
Ronan Docherty (Imperial College London, UK) |
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| Upsampling DINOv2 features for unsupervised vision tasks and weakly supervised materials segmentation
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| 11 |
Engin Durgun (Bilkent University UNAM, Turkey) |
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| Two-Dimensional 2gamma-In2Se3 in Bilayer-like Coloring Triangle Lattice: Mechanical, Electronic, Transport, and Photocatalytic Properties
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| 6 |
Hendrik Ehrich (TU Wien, Austria) |
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| From alloy behavior to deformation twinning and beyond: MD simulations and machine learning for tribological insights
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| 45 |
Amaia Elizaran Mendarte (Centro de Física de Materiales (CSIC-UPV/EHU), Spain) |
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| Predicting molecular properties using Recurrent Neural Networks under data scarcity scenarios
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| 22 |
Martin Boerstad Eriksen (IFAE-PIC, Spain) |
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| Application of ML based denoise algorithms to the EELS data of the 3rd generation Medium Mn Steel
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| 14 |
Dorye L. Esteras (Catalan Institute of Nanoscience and Nanotechnology, Spain) |
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| Towards automatic workflows to accelerate the discovery of quantum materials
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| 42 |
Kiarash Farajzadehahary (Polymat - UPV/EHU, Spain) |
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| Machine Learning Models for Predicting Key Properties in Free Radical Emulsion Polymerization
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| 16 |
Thomas Jean-François Galvani (Catalan Institute of Nanoscience and Nanotechnology, Spain) |
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| Dielectric properties in models of amorphous Boron Nitride
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| 15 |
Jaime Garrido (Catalan Institute of Nanoscience and Nanotechnology, Spain) |
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| Studying 2D magnetic materials with high-throughput automated workflows from Density Functional Theory
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| 23 |
Diego Alejandro Garzón Castellanos (INL - International Iberian Nanotechnology Laboratory, Portugal) |
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| Data-driven tolerance factor for chalcogenide perovskites and their suitability for photovoltaic applications
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| 50 |
David Gryc (Technical University Munich, Germany) |
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| AI Accelerated Study of MOF-derived Composites for Supercapacitors
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| 40 |
Eduardo Hernandez (CSIC - ICMM, Spain) |
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| Spectrum Reconstruction through Machine Learning
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| 18 |
Stefaan Hessmann (TU Berlin, Germany) |
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| Accelerating crystal structure search through active learning with neural networks for rapid relaxations
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| 33 |
Rina Ibragimova (Aalto University, Finland) |
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| General-purpose ML interatomic potential for CH and CHO: unifying the description of organic materials and molecules
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| 7 |
Mikel Irigoyen (POLYMAT & Univesidad del Pais Vasco (UPV/EHU), Spain) |
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| Predicting the Stress-Strain behaviour of isotactic Polypropylene (iPP) by using Molecular Dynamics and Neural Networks
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| 28 |
Ashna Jose (Imperial College London, UK) |
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| Data-driven design of electroactive metal-organic frameworks
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| 56 |
Jolla Kullgren (Uppsala University, Sweden) |
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| Correlating spectra to structure for water in, and on, crystals -Predictions and/or insight?
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| Late 1 |
Ashwani Kushwaha (IIT Bombay, India) |
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| Modeling Biphenylene Networks(BPN) with SNAP-Based Machine Learning Potential
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| 19 |
Rachid Laref (laboratoire UCCS, Université d´Artois , France) |
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| Natural Deep Eutectic Solvents Design Acceleration Using Variational Auto Encoder
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| 3 |
Jonas Lederer (Technical University Berlin, Germany) |
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| Monitoring Framework for Molecular Manipulation Procedures
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| 1 |
Valerie Levine (Uppsala University, Sweden) |
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| Machine learning-based image analysis of semisolid extrusion (SSE) pharmaceutical tablets on a tapering schedule
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| 47 |
Zhenzhu Li (Imperial College London, UK) |
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| Configurational design of high PCE chalcogenides via Reinforcement learning
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| 12 |
Jian Xiang Lian (CIC energiGUNE, Spain) |
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| Insights on the ionic transport and interface stability of halide solid electrolytes interfaces from machine learning force field simulations
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| 54 |
Kinga Mastej (Imperial College London, UK) |
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| Generative models for crystalline materials design
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| 17 |
Jesus Inocente Medina Santos (Trinity College Dublin, Ireland) |
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| Beyond lithium: Enhancing material development by artificial intelligence
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| 26 |
Thomas Nicholas (Ghent University, Belgium) |
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| Modelling complex, stimuli-induced order–disorder transitions in metal–organic frameworks
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| 25 |
Mathias Stokkebye Nissen (Technical University of Denmark, Denmark) |
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| Exploring Iridium-Doped ZrO₂ Structures Using an Interatomic Potential-Based Workflow
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| 39 |
Raul Ortega-Ochoa (Technical University of Denmark, Denmark) |
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| MolMiner: Transformer architecture for fragment-based autoregressive generation of molecular stories
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| 52 |
Eva Ortiz Mansilla (Universidad Autónoma de Madrid, Spain) |
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| Deep Reinforcement Learning for Radiative Heat Transfer Optimization Problems
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| 31 |
Suraj Panja (ICIQ, Spain) |
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| Operando modeling of materials as a function of reaction conditions using NNP
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| 24 |
Inchul Park (POSCO holding Research Center, South Korea) |
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| Interpretable Machine Learning Framework: Unveiling Redox Mechanisms in Lithium-Rich Layered Cathodes
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| 36 |
Laura-Bianca Pasca (University of Oxford, UK) |
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| Machine-learning-driven modelling of amorphous and polycrystalline BaZrS3
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| 30 |
Pablo Peña (CIC nanogune, Spain) |
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| Structure of water around graphene nanoribbons from ab initio machine learning simulations
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| 5 |
Alastair Price (University of Toronto, Canada) |
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| System-Specific Dispersion Damping for Enhanced Accuracy in DFT Calculations of Noncovalent Interactions
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| 27 |
Jesper Rask Pedersen (DTU Energy, Technical University of Denmark, Denmark) |
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| Designing High Entropy Oxides for Fuel Cell Using Machine Learning Potential
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| 13 |
Marc Raventós (ALBA-CELLS, Spain) |
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| xrd_simulator: Towards a PyTorch-based framework for FEM simulation of crystalline materials and diffraction experiments
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| 8 |
Luis Ricardez-Sandoval (University of Waterloo, Canada) |
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| Bimetallic Transition-Metal-Doped CeO2 for the Reverse Water-Gas Shift Reaction: A Density Functional Theory Analysis
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| 48 |
Francesco Ricci (UCLouvain/Matgenix, Belgium) |
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| A Domain-Specific Chatbot for atomistic simulations: Enhancing Accessibility and Productivity Using LLMs and RAG
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| 53 |
Charalampos Sarimveis (National Technical University of Athens, Greece) |
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| A Machine Learning Pipeline for estimating Binding Affinity to Serum Albumin and Half-Lives of Per- and Polyfluoroalkyl Substances in Humans
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| 20 |
Florian Simperl (TU Wien, Austria) |
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| Convolutional neural network for high-throughput materials characterization with X-ray photoelectron spectroscopy using the Simulation of Electron Spectra for Surface Analysis code
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| 46 |
Jaume Alexandre Solé Gómez (Universitat de Barcelona, Spain) |
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| Cartesian Encoding Graph Neural Network for Crystal Structures Property Prediction: Application to Thermal Ellipsoid Estimation
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| 55 |
Daniel Speckhard (FHI of the Max Planck Society / MPI FKF / HU Berlin, Germany) |
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| Extrapolation to the complete basis-set limit in DFT using statistical learning
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| 44 |
Muhammad Usman (BCMaterials, Spain) |
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| Generating Machine Learning Force-Fields for colloidal Quantum Dots. The case for CdSe, PbSe and InP
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| 34 |
Elohan Veillon (Université d´Artois, France) |
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| Ab-Initio metrics pipeline for the Evaluation of Material Generative Models
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| 29 |
Shirui Wang (Imperial College London, UK) |
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| Fine-tuning universal force fields for rapid and accurate lattice thermal conductivity
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| 38 |
Litong Wu (University of Oxford, UK) |
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| Understanding the Structure of Amorphous Na–P Battery Anodes with Machine Learning
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| 41 |
Haochen Yu (Université catholique de Louvain, Belgium) |
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| Systematic assessment of various universal machine-learning interatomic potentials
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| 35 |
Qian Yu (Tongji University, China) |
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| Machine Learning–Accelerated Prediction of Amorphization Enthalpy in Ionic Compounds
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| 9 |
Wanqi Zhou (CIC nanogune, Spain) |
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| Structure and dynamics of water at feldspar surfaces from machine learning augmented molecular simulation
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| 37 |
Ivan Žugec (Materials Physics Center, Spain) |
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| Dynamic training enhances machine learning potentials for long-lasting molecular dynamics
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