|
|
|
Stephan Abermann (AIT Austrian Institute of Technology GmbH, Austria) |
|
| ARA - AIT Research Acceleration Platform
|
|
|
|
|
|
Mohammed Saif Ali Al-Fahdi (Federal Institute for Materials Research and Testing (BAM), Germany) |
|
| Chemically-Inspired Bonding Features in MEGNet Enhance Materials Properties Predictions
|
|
|
|
|
|
Bashar Albakri (Bundesanstalt für Materialforschung und -prüfung, Germany) |
|
| Exploring Transfer Learning for Materials Property Prediction
|
|
|
|
|
|
Amil Aligayev (NOMATEN CoE, National Centre for Nuclear Research (NCBJ), Poland) |
|
| Influence of Hf on the microstructure and properties of HfxMoTaW medium-entropy alloys: A Multiscale AI-Integrated Approach
|
|
|
|
|
|
Faisal Alresheedi (Qassim University, Saudi Arabia) |
|
| AI-Assisted Prediction of Hardness in Nitrogen-Alloyed Austenitic Stainless Steel Thin Films
|
|
|
|
|
|
Mira Baraket (ATLANT 3D, Denmark) |
|
| Direct Atomic Layer Processing (DALP™) for Rapid Combinatorial Libraries and Device-Ready Materials Evaluation
|
|
|
|
|
|
Nevena Cirkovic (Technological University of the Shannon:Midwest, Ireland) |
|
| Numerical modelling of atomization through an aperture plate in an active vibrating mesh nebuliser
|
|
|
|
|
|
Marco Coïsson (INRIM, Italy) |
|
| Specific loss power of magnetic nanoparticles: a machine learning approach
|
|
|
|
|
|
Josep Cruañes Giner (ICN2, Spain) |
|
| NanoDetector: Deep Learning pipeline for automated nanoparticle location, tracking and imaging
|
|
|
|
|
|
Beatriz Cuadrado Benavent (Software for Chemistry & Materials / Vrije Universiteit Amsterdam, The Netherlands) |
|
| Fine-Tuning MLIPs for Reactive MD in Catalytic Surface Chemistry
|
|
|
|
|
|
Hieu-Chi Dam (Japan Advanced Institute of Science and Technology, Japan) |
|
| Single-shot Coherent X-ray Diffraction Imaging of Dynamic Material Phenomena via Self-Supervised Phase Retrieval
|
|
|
|
|
|
Duc-Anh Dao (Japan Advanced Institute of Science and Technology, Japan) |
|
| Material Dynamics Analysis with Deep Generative Model
|
|
|
|
|
|
Laura Isabel de Eugenio Martinez (Margarita Salas Center for Biological Research (CSIC), Spain) |
|
| Beyond Trial-and-Error: Artificial Intelligence for PHA Depolymerase Engineering
|
|
|
|
|
|
Panyalak Detrattanawichai (Imperial College London, UK) |
|
| Data-driven exploration of halide spinels for high performance ionic conductors
|
|
|
|
|
|
Davide Di Stefano (Ansys (Synopsys), UK) |
|
| Filling the Gaps: ML Tabular Regression for Missing Material Properties in Data‑Scarce Engineering Contexts
|
|
|
|
|
|
Venkata Sai Subhash Ganti (University of Bayreuth, Germany) |
|
| Machine Learning-Accelerated Discovery of Sustainable Redox-Active Polymers for Next-Generation Batteries
|
|
|
|
|
|
Arya García Esteban (Instituto de Ciencia de Materiales de Madrid (ICMM-CSIC), Spain) |
|
| Deep Learning for Molecular DFT
|
|
|
|
|
|
Niklas Gebauer (Technische Universität Berlin / BIFOLD, Germany) |
|
| SchNetPack 3.0: A Neural Network Toolbox for Predictive and Generative Atomistic ML
|
|
|
|
|
|
Shulai Guo (CIC nanogune, Spain) |
|
| Molecular insight into crystal nucleation during cement hydration from ab initio machine-learning simulations
|
|
|
|
|
|
Minh-Quyet Ha (Japan Advanced Institute of Science and Technology, Japan) |
|
| From Uncertainty to Discovery: Integrating Multiple Evidence Sources for AI-Driven Materials Science
|
|
|
|
|
|
Yuanchao Hu (Dongguan Institute of Materials Science and Technology, China) |
|
| Material Network Science as A New Paradigm For Matter Design
|
|
|
|
|
|
Timothée Jamin (Aalborg University, Denmark) |
|
| Spontaneous defect formation as the origin of the superionic transition in antifluorite structures
|
|
|
|
|
|
Namrata Jaykhedkar (Bundesanstalt für Materialforschung und -prüfung (BAM), Germany) |
|
| Atomistic interaction at the interface between Li6PS5Cl and Li metal in solid state batteries
|
|
|
|
|
|
Ashna Jose ( Imperial College London, UK) |
|
| Transfer Learning of a Universal Hamiltonian Graph Neural Network for Metal-Organic Frameworks
|
|
|
|
|
|
Jiban Kangsabanik (Aalborg University, Denmark) |
|
| Role of Disorder in Defect Energetics of Solid-State Electrolytes: A First-Principles Perspective
|
|
|
|
|
|
Michal Kaufman (University of West Bohemia in Pilsen, Czech Republic) |
|
| Generative AI Meets Phonon Validation: A Multi-Stage Workflow for Reliable Discovery of Hydrogen-Storage Hydrides
|
|
|
|
|
|
Giaan Kler-Young (University of Cambridge, UK) |
|
| What is the best density functional for adsorption?
|
|
|
|
|
|
Mathilde Kretz (ENS, France) |
|
| Reactive Machine-learned potentials: optimal active learning strategies development and application to HMX energetic material
|
|
|
|
|
|
Anastasia Kryachkova (University of Amsterdam, The Netherlands) |
|
| Benchmarking Transport Properties of Ionic Liquids with a Universal Machine Learning Force Field: SO3LR vs. Classical Force Fields for EMIM NTf₂
|
|
|
|
|
|
Abhishek Kumar (IIT Bombay, India) |
|
| Modeling Magnesium Silicide (Mg2Si) with SNAP-Based Machine Learning Potential
|
|
|
|
|
|
Francesco La Porta (Synchrotron Soleil, France) |
|
| Automated Multi-Element composition analysis of X-Ray Fluorescence Spectra via Vision Transformers
|
|
|
|
|
|
Menglei Li (Harbin Institute of Technology, China) |
|
| A Deep Learning Framework for Predicting the Mechanical Properties of Discontinuous Fiber-Reinforced Composites
|
|
|
|
|
|
Tianshu Li (Imperial College London, UK) |
|
| Data-Driven Crystal Structure Prediction for Ternary Metal Chalcogenides
|
|
|
|
|
|
Tingwei Li (Imperial College London, UK) |
|
| Anisotropy in phonon and electron transport in Sb2Se3 from machine learning foundation models
|
|
|
|
|
|
Cibrán López (Universitat Politècnica de Catalunya, Spain) |
|
| Machine Learning-Aided Band Edge Engineering in Pictogen Chalcohalides
|
|
|
|
|
|
Xuliang Luo (Aalto University, Finland) |
|
| Data-Driven Prediction of Metallic Glass Forming Ability via Bayesian Inference
|
|
|
|
|
|
Luis Martin Encinar (Universidad de Valladolid, Spain) |
|
| Modelling Hydrogen-Transition Metal Interactions on Carbon Platforms with Universal ML Interatomic Potentials
|
|
|
|
|
|
Matilda Martinez Arellanes (DTU Chemistry, Denmark) |
|
| Data-Driven Development of High-Entropy Spinel Oxide Catalysts for CO2 Utilization
|
|
|
|
|
|
Ines Mezghani (Ecole Normale Supérieure PSL, France) |
|
| Electrical Double Layer at the Air–Water Interface: A machine-learning interatomic potential simulation study
|
|
|
|
|
|
Sneha Mittal (Technical University of Denmark, Denmark) |
|
| How Water Tunes Quantum Transport in Nanoporous Graphenes: An Artificial Intelligence Approach
|
|
|
|
|
|
Kourosh Mobredi (Aalto University, Finland) |
|
| Data-Efficient Bayesian Optimization for Improving the Functional Properties of Cellulosic Foams
|
|
|
|
|
|
Evgeny Moerman (Université du Luxembourg, Luxembourg) |
|
| Many-body dispersion from machine learning for molecules and materials
|
|
|
|
|
|
Doaa Mohamed (Ruhr University, Germany) |
|
| Cold-Starting Active Learning Loops Using Multiple Data Modalities
|
|
|
|
|
|
Gonzalo Nicanor Molina (IMDEA Nanociencia , Spain) |
|
| First-principles calculations of magnetic defects in rare-earth-doped Bi2Te3
|
|
|
|
|
|
Juan Morales López (Instituto de Ciencia de Materiales de Madrid - CSIC, Spain) |
|
| Molecular Dynamics simulations of aqueous Deep Eutectic Solvents: foundations for Machine Learning screening of High Performance Electrolytes
|
|
|
|
|
|
Aliasghar Najafzadehkhoee (Institute of Inorganic Chemistry Slovak Academy of Sciences, Slovakia) |
|
| Impact-Damage Resistance of Pharmaceutical Glass Vials via FEM–Machine Learning Co-Design
|
|
|
|
|
|
Masahiro Negishi (Imperial College London, UK) |
|
| Quantifying Structural Novelty via Element Substitutions for AI-generated Crystals
|
|
|
|
|
|
Aneta Niklas (University of Oxford, UK) |
|
| Modelling of Cellulose Materials Using Graph-Based Interatomic Potentials
|
|
|
|
|
|
Kaifeng Niu (University of Cambridge, UK) |
|
| Revealing the role of surface disorder in H2 desorption from metal surfaces via machine learning enhanced simulation
|
|
|
|
|
|
Luoxuan Peng (University of Modena and Reggio Emilia, Italy) |
|
| Atomistic Simulation of Ge/SiGe Interfaces for Quantum Technology Devices
|
|
|
|
|
|
Hugo Salazar-Lozas (Institute of Chemical Research of Catalonia, Spain) |
|
| Probing the limits of the Universal Models for Atoms: energetic and structural analysis of polyoxometalates
|
|
|
|
|
|
Haralambos Sarimveis (National Technical University of Athens, Greece) |
|
| A Model Predictive Control-Inspired Framework for Generative Multi-Objective Chemical and Materials Design
|
|
|
|
|
|
Jörg Schaarschmidt (Karlsruhe Institute of Technology (KIT), Germany) |
|
| Shaping the Future of AI-EnabledDigital Workflows in Material Science
|
|
|
|
|
|
Bryan Siu (University of Bristol, UK) |
|
| A Microstructure-Informed GRU- Based Autoregressive Framework for Constitutive Modelling
|
|
|
|
|
|
George E.H. Smith (University of Birmingham, UK) |
|
| Is Generative AI a Game-Changer for Computational Materials Discovery of New Solid-State Materials?
|
|
|
|
|
|
Claudia Solek Pondo (Universidad Nacional de Educación a Distancia (UNED), Spain) |
|
| Data-driven framework towards an AI-assisted multiparametric qualification for FFF-processed components
|
|
|
|
|
|
Jose M Soler (Universidad Autonoma de Madrid, Spain) |
|
| An Electron Force Field for molecular and electron dynamics
|
|
|
|
|
|
Kaihong Sun (Aalborg University, Denmark) |
|
| Accessing the effect of local order on the order-disorder phase transition in chalcopyrites
|
|
|
|
|
|
Katharina Ueltzen (Federal Institute for Materials Research and Testing, Germany) |
|
| Can simple exchange heuristics guide us in the machine learning of magnetic properties of solids?
|
|
|
|
|
|
Tien Sinh Vu (Japan advanced institute of science and technology, Japan) |
|
| Interpretable AI for Quantum Materials Design: Attention-Driven Discovery of Structure–Property Correlations from First-Principles Simulations
|
|
|
|
|
|
Matthew Walker (UCL, UK) |
|
| AI-Driven Discovery and Characterisation of Ferroelectric Photovoltaics
|
|
|
|
|
|
Luc Walterbos (Bundesanstalt für Materialforschung und -prüfung (BAM), Germany) |
|
| (U)Mapping the chemical landscape of Halide Double Perovskites
|
|
|
|
|
|
Weike Ye (Toyota Research Institute, USA) |
|
| Seeing without Crystal Structure: Multimodal AI for Materials Characterization
|
|
|
|
|
|
Qian Yu (Tongji University, China) |
|
| Neutron PDF–Constrained Atomic Modelling of Amorphous Solid-State Electrolytes
|
|
|
|
|
|
Lei Zhang (Ruhr-Universität Bochum, ICAMS, Germany) |
|
| Literature-Based Prediction of High-Performance Electrocatalysts
|
|
|
|
|
|
Wanqi Zhou (CIC nanogune, Spain) |
|
| Molecular mechanism of heterogeneous ice nucleation in the atmosphere
|
|
|
|
|
| 55/66 |
| |
