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Posters (67) - Alphabetical order |
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| Poster nº |
Author & Title |
Abstract |
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| 23 |
Stephan Abermann (AIT Austrian Institute of Technology GmbH, Austria) |
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| ARA - AIT Research Acceleration Platform
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| 38 |
Mohammed Saif Ali Al-Fahdi (Federal Institute for Materials Research and Testing (BAM), Germany) |
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| Chemically-Inspired Bonding Features in MEGNet Enhance Materials Properties Predictions
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| 33 |
Amil Aligayev (NOMATEN CoE, National Centre for Nuclear Research (NCBJ), Poland) |
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| Influence of Hf on the microstructure and properties of HfxMoTaW medium-entropy alloys: A Multiscale AI-Integrated Approach
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| 6 |
Vedad Babic (KAI GmbH, Austria) |
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| Predicting Formation Energies using Universal Machine Learned Interatomic Potentials
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| 7 |
Nevena Cirkovic (Technological University of the Shannon:Midwest, Ireland) |
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| Numerical modelling of atomization through an aperture plate in an active vibrating mesh nebuliser
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| 62 |
Marco Coïsson (INRIM, Italy) |
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| Specific loss power of magnetic nanoparticles: a machine learning approach
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| Late 2 |
Adam Coxson (ICN2, Spain) |
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| An overview of state of the art developments in ML-Hamiltonians for solid-state systems
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| 39 |
Josep Cruañes Giner (ICN2, Spain) |
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| NanoDetector: Deep Learning pipeline for automated nanoparticle location, tracking and imaging
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| 8 |
Beatriz Cuadrado Benavent (Software for Chemistry & Materials / Vrije Universiteit Amsterdam, The Netherlands) |
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| Fine-Tuning MLIPs for Reactive MD in Catalytic Surface Chemistry
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| 9 |
Susi Cuccurullo (Istituto Català de Nanociencia y Nanotecnologia, Spain) |
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| Evaluation of Foundation Models for van der Waals Heterostructure Moirè Supercells
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| 34 |
Hieu-Chi Dam (Japan Advanced Institute of Science and Technology, Japan) |
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| Single-shot Coherent X-ray Diffraction Imaging of Dynamic Material Phenomena via Self-Supervised Phase Retrieval
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| 24 |
Duc-Anh Dao (Japan Advanced Institute of Science and Technology, Japan) |
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| Material Dynamics Analysis with Deep Generative Model
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| 3 |
Laura Isabel de Eugenio Martinez (Margarita Salas Center for Biological Research (CSIC), Spain) |
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| Beyond Trial-and-Error: Artificial Intelligence for PHA Depolymerase Engineering
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| 25 |
Panyalak Detrattanawichai (Imperial College London, UK) |
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| Data-driven exploration of halide spinels for high performance ionic conductors
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| 63 |
Davide Di Stefano (Ansys (Synopsys), UK) |
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| Filling the Gaps: ML Tabular Regression for Missing Material Properties in Data‑Scarce Engineering Contexts
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| 40 |
Ahmed Elhag (University of Oxford, UK) |
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| Learning Inter-Atomic Potentials without Explicit Equivariance
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| 41 |
Venkata Sai Subhash Ganti (University of Bayreuth, Germany) |
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| Machine Learning-Accelerated Discovery of Sustainable Redox-Active Polymers for Next-Generation Batteries
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| 10 |
Arya García Esteban (Instituto de Ciencia de Materiales de Madrid (ICMM-CSIC), Spain) |
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| Deep Learning for Molecular DFT
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| 42 |
Niklas Gebauer (Technische Universität Berlin / BIFOLD, Germany) |
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| SchNetPack 3.0: A Neural Network Toolbox for Predictive and Generative Atomistic ML
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| 43 |
Shulai Guo (CIC nanogune, Spain) |
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| Molecular insight into crystal nucleation during cement hydration from ab initio machine-learning simulations
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| 26 |
Minh-Quyet Ha (Japan Advanced Institute of Science and Technology, Japan) |
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| From Uncertainty to Discovery: Integrating Multiple Evidence Sources for AI-Driven Materials Science
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| Late 1 |
Jad Jaafar (University of Cambridge, UK) |
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| Accelerated Reaction Exploration across Scales: a Hybrid Operando and Modelling Study of Oxidation Kinetics in Monolayer Tungsten Disulfide
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| 11 |
Timothée Jamin (Aalborg University, Denmark) |
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| Spontaneous defect formation as the origin of the superionic transition in antifluorite structures
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| 12 |
Namrata Jaykhedkar (Bundesanstalt für Materialforschung und -prüfung (BAM), Germany) |
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| Atomistic interaction at the interface between Li6PS5Cl and Li metal in solid state batteries
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| 59 |
Ashna Jose ( Imperial College London, UK) |
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| Transfer Learning of a Universal Hamiltonian Graph Neural Network for Metal-Organic Frameworks
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| 44 |
Michal Kaufman (University of West Bohemia in Pilsen, Czech Republic) |
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| Generative AI Meets Phonon Validation: A Multi-Stage Workflow for Reliable Discovery of Hydrogen-Storage Hydrides
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| 14 |
Giaan Kler-Young (University of Cambridge, UK) |
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| What is the best density functional for adsorption?
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| 45 |
Mathilde Kretz (ENS, France) |
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| Reactive Machine-learned potentials: optimal active learning strategies development and application to HMX energetic material
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| 46 |
Anastasia Kryachkova (University of Amsterdam, The Netherlands) |
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| Benchmarking Transport Properties of Ionic Liquids with a Universal Machine Learning Force Field: SO3LR vs. Classical Force Fields for EMIM NTf₂
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| 35 |
Francesco La Porta (Synchrotron Soleil, France) |
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| Automated Multi-Element composition analysis of X-Ray Fluorescence Spectra via Vision Transformers
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| 4 |
Menglei Li (Harbin Institute of Technology, China) |
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| A Deep Learning Framework for Predicting the Mechanical Properties of Discontinuous Fiber-Reinforced Composites
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| 27 |
Tianshu Li (Imperial College London, UK) |
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| Data-Driven Crystal Structure Prediction for Ternary Metal Chalcogenides
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| 47 |
Tingwei Li (Imperial College London, UK) |
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| Anisotropy in phonon and electron transport in Sb2Se3 from machine learning foundation models
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| 48 |
Cibrán López (Universitat Politècnica de Catalunya, Spain) |
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| Machine Learning-Aided Band Edge Engineering in Pictogen Chalcohalides
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| 28 |
Xuliang Luo (Aalto University, Finland) |
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| Data-Driven Prediction of Metallic Glass Forming Ability via Bayesian Inference
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| 49 |
Luis Martin Encinar (Universidad de Valladolid, Spain) |
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| Modelling Hydrogen Storage on 2D Carbon Platforms with Universal MLIPs.
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| 29 |
Matilda Martinez Arellanes (DTU Chemistry, Denmark) |
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| Data-Driven Development of High-Entropy Spinel Oxide Catalysts for CO2 Utilization
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| 15 |
Ines Mezghani (Ecole Normale Supérieure PSL, France) |
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| Electrical Double Layer at the Air–Water Interface: A machine-learning interatomic potential simulation study
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| 50 |
Sneha Mittal (Technical University of Denmark, Denmark) |
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| How Water Tunes Quantum Transport in Nanoporous Graphenes: An Artificial Intelligence Approach
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| 51 |
Kourosh Mobredi (Aalto University, Finland) |
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| Data-Efficient Bayesian Optimization for Improving the Functional Properties of Cellulosic Foams
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| 52 |
Evgeny Moerman (Université du Luxembourg, Luxembourg) |
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| Many-body dispersion from machine learning for molecules and materials
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| 53 |
Doaa Mohamed (Ruhr University, Germany) |
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| Cold-Starting Active Learning Loops Using Multiple Data Modalities
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| Late 3 |
Gonzalo Nicanor Molina (IMDEA Nanociencia , Spain) |
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| First-principles calculations of magnetic defects in rare-earth-doped Bi2Te3
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| 54 |
Francisco Antonio Molina Bakhos (ICN2, Spain) |
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| Nanoparticles image analysis with ms2nano
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| 64 |
Juan Morales López (Instituto de Ciencia de Materiales de Madrid - CSIC, Spain) |
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| Molecular Dynamics simulations of aqueous Deep Eutectic Solvents: foundations for Machine Learning screening of High Performance Electrolytes
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| 16 |
Aliasghar Najafzadehkhoee (Institute of Inorganic Chemistry Slovak Academy of Sciences, Slovakia) |
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| Impact-Damage Resistance of Pharmaceutical Glass Vials via FEM–Machine Learning Co-Design
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| 55 |
Masahiro Negishi (Imperial College London, UK) |
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| Quantifying Structural Novelty via Element Substitutions for AI-generated Crystals
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| 17 |
Aneta Niklas (University of Oxford, UK) |
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| Modelling of Cellulose Materials Using Graph-Based Interatomic Potentials
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| 18 |
Kaifeng Niu (University of Cambridge, UK) |
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| Revealing the role of surface disorder in H2 desorption from metal surfaces via machine learning enhanced simulation
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| 56 |
Luoxuan Peng (University of Modena and Reggio Emilia, Italy) |
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| Atomistic Simulation of Ge/SiGe Interfaces for Quantum Technology Devices
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| 2 |
Eros Radicchi (University of Verona, Italy) |
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| Physics-Informed Neural Networks for the Estimation of Nanoparticles Growth Kinetics
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| 19 |
Hugo Salazar-Lozas (Institute of Chemical Research of Catalonia, Spain) |
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| Probing the limits of the Universal Models for Atoms: energetic and structural analysis of polyoxometalates
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| 61 |
Haralambos Sarimveis (National Technical University of Athens, Greece) |
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| A Model Predictive Control-Inspired Framework for Generative Multi-Objective Chemical and Materials Design
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| 30 |
Jörg Schaarschmidt (Karlsruhe Institute of Technology (KIT), Germany) |
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| Shaping the Future of AI-EnabledDigital Workflows in Material Science
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| 20 |
Bryan Siu (University of Bristol, UK) |
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| A Microstructure-Informed GRU- Based Autoregressive Framework for Constitutive Modelling
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| 65 |
George E.H. Smith (University of Birmingham, UK) |
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| Is Generative AI a Game-Changer for Computational Materials Discovery of New Solid-State Materials?
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| 1 |
Claudia Solek Pondo (Universidad Nacional de Educación a Distancia (UNED), Spain) |
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| Data-driven framework towards an AI-assisted multiparametric qualification for FFF-processed components
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| 21 |
Jose M Soler (Universidad Autonoma de Madrid, Spain) |
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| An Electron Force Field for molecular and electron dynamics
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| 22 |
Kaihong Sun (Aalborg University, Denmark) |
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| Accessing the effect of local order on the order-disorder phase transition in chalcopyrites
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| 57 |
Katharina Ueltzen (Federal Institute for Materials Research and Testing, Germany) |
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| Can simple exchange heuristics guide us in the machine learning of magnetic properties of solids?
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| 60 |
Tien Sinh Vu (Japan advanced institute of science and technology, Japan) |
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| Interpretable AI for Quantum Materials Design: Attention-Driven Discovery of Structure–Property Correlations from First-Principles Simulations
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| 31 |
Matthew Walker (UCL, UK) |
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| AI-Driven Discovery and Characterisation of Ferroelectric Photovoltaics
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| 32 |
Luc Walterbos (Bundesanstalt für Materialforschung und -prüfung (BAM), Germany) |
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| (U)Mapping the chemical landscape of Halide Double Perovskites
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| 36 |
Weike Ye (Toyota Research Institute, USA) |
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| Seeing without Crystal Structure: Multimodal AI for Materials Characterization
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| 37 |
Qian Yu (Tongji University, China) |
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| Neutron PDF–Constrained Atomic Modelling of Amorphous Solid-State Electrolytes
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| 5 |
Lei Zhang (Ruhr-Universität Bochum, ICAMS, Germany) |
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| Literature-Based Prediction of High-Performance Electrocatalysts
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| 58 |
Wanqi Zhou (CIC nanogune, Spain) |
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| Molecular mechanism of heterogeneous ice nucleation in the atmosphere
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