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Posters (24) - Alphabetical order |
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| Poster nº |
Author & Title |
Abstract |
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| 8 |
Albert Abio (Eurecat: Technology Centre of Catalonia Eurecat, Spain) |
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Graph Neural Network-Based Surrogate Model of Hot Stamping Finite-Element Simulations
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| 22 |
Isaac Alcón (Institut Catala de Nanociencia i Nanotecnologia (ICN2), Spain) |
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Colossal quantum transport anisotropy in nanoporous graphenes at room temperature
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| 9 |
Oskar Andersson (Linköping University, Sweden) |
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Generative AI models for property to structure materials prediction
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| 21 |
Mohammed Benaissa (Université de Rennes, CNRS, IPR (Institut de Physique de Rennes) - UMR 6251 F-35000, France) |
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Reducing Computational time in 2D Material DFT Simulation with Charge Mixing Optimization via Bayesian Algorithm
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| 10 |
Juan Camilo Buitrago Diaz (Universidad de Ibagué, Colombia) |
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Exploring the Potential of Mask Region-based Convolutional Neural Network in Identifying Twins in Shape Memory Alloys
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| Late 1 |
Adam Coxson (University of Liverpool, UK) |
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Machine Learning the Fock Matrix in the Atomic Orbital Basis for extended pi-conjugated molecules within a Self-Consistent Framework
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| 2 |
Dana Engelgardt (Kyungpook National University, South Korea) |
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Spin polarisation in large-angle twisted bilayer graphene on nickel substrate
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| 19 |
Tommaso Forni (CNR - ISMN, Italy) |
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GrapheNet: A Novel Deep Learning Model for Predicting Physical and Electronic Properties of 2D Materials Using Images
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| 23 |
Jose Hugo Garcia Aguilar (Institut Catala de Nanociencia i Nanotecnologia (ICN2), Spain) |
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Exploring Quantum Property-Data Correlations in Metal Organic Frameworks using Unsupervised Learning
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| 3 |
Jaime Garrido Aldea (Institut Catala de Nanociencia i Nanotecnologia (ICN2), Spain) |
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Development of an automated workflow for well converged DFT calculations using SIESTA and the AIIDA infrastructure satisfying FAIR data principles
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| 11 |
Abdelkader Kara (University of Central Florida, USA) |
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Using Machine Learning for Advanced Materials: The Case of High-Entropy Alloys
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| 12 |
Peder Lyngby (DTU, Denmark) |
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Exploring 2D Materials: Discovery and Characterization via Generative Models and DFT
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| 13 |
Artem Maevskiy (National University of Singapore, Institute for Functional Intelligent Materials, Singapore) |
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Topological Analysis of Machine-Learned Interatomic Potential for Solid Electrolytes Discovery
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| 1 |
Marco Moors (Leibniz Institute of Surface Engineering (IOM), Germany) |
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Polyoxovanadates as redox-active 2D materials for memristive applications
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| 7 |
Ilias-Panagiotis Oikonomou (Trinity College Dublin, Ireland) |
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Identifying point defects in liquid-phase exfoliated PtSe2
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| 14 |
Swapneel Amit Pathak (Max-Planck Institute for the Structure and Dynamics of Matter, Germany) |
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Unsupervised clustering of magnetisation vector fields
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| 4 |
Gabriel Persson (Linköping University, IFM, Sweden) |
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High-throughput Computational Workflows for Screening Fluoride Perovskites for use in Piezoelectrics.
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| 15 |
William Sandholt Hansen (Technical University of Denmark, Denmark) |
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Computational design of stacking oxide free-standing membranes into artificial heterostructures
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| 5 |
Ari Paavo Seitsonen (École Normale Supérieure, France) |
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Atomistics Simulation of Vibrational Signals in Liquid Water and Aqueous Solutions Using Machine Learning Potentials
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| 16 |
Juan José Seoane (Universitad Autónoma de Barcelona, Spain) |
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Exploring Neural Networks for Predicting Bohmian Trajectories in Many-Body Scenarios
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| 20 |
Kartikeya Sharma (Technical University of Denmark, Denmark) |
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Predicting properties of 2D materials using graph neural network
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| 6 |
Gohar Ali Siddiqui (Technical University of Munich, Germany) |
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Enhancing the identification of collective variables and their interpretability using machine learning in molecular dynamics
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| 17 |
Matias Oscar Volman Stern (Aalen University, Germany) |
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Synthetic labeled dataset generation for semantic segmentation of materials micrographs.
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| 18 |
Mario Vozza (Polytechnic University of Turin, Italy) |
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Efficient Workflow Automation for Materials Modeling: Towards Predictive AI Systems Using High Throughput Synthetic Dataset Generation
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| 24/24 |
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