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| Plenary |
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Ivan Cole (RMIT University, Australia)
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Refining Molecular Characterization to allow machine learning of the effectiveness of corrosion inhibitors
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Marek Grzelczak (Centro de Fisica de Materiales (CSIC-UPV/EHU), Spain)
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Machine Learning for Nanoparticle Synthesis
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Javier Heras-Domingo (ICIQ, Spain)
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Unlocking the Potential of EXAFS: Machine Learning Approaches for Spectroscopic Data
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Michael Alejandro Hernandez Bertran ((1) FIM, University of Modena and Reggio Emilia, (2) Istituto Nanoscienze, Consiglio Nazionale delle Ricerche CNR, Italy)
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An X-ray spectra simulations workflow based on machine learning: applications to Li-ion battery materials
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Nicholas Hine (University of Warwick, UK)
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Understanding Domain Reconstruction of Twisted Bilayer and Heterobilayer Transition Metal Dichalcogenides through Machine Learned Interatomic Potentials
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Martin Hoffmann Petersen (Technical University of Denmark, Denmark)
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Quest for outperforming cathode materials for Sodium-ion batteries
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Zahra Khatibi (Trinity College Dublin , Ireland)
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Evolutionary Recipe: Designing Single Molecule Magnets for Spintronics
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Ivan Kruglov (Emerging Technologies Research Center, XPANCEO, United Arab Emirates)
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AI-guided screening of van der Waals materials with high optical anisotropy
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Artem Mishchenko (The University of Manchester, UK)
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Deep Learning Electronic Fingerprints for Mapping Flat-Band Materials in 2D and 3D Databases
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Vincenzo Palermo (CNR-ISOF, Italy)
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Multivariate sensing of sodium and potassium ions using Prussian blue, graphene oxide electrodes and machine learning
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Luiz Felipe Pereira (Universidade Federal de Pernambuco, Brazil)
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Modeling heat transport in amorphous Ge2Sb2Te5 with a deep neural network interatomic potential
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Antonio Rossi (Istituto Italiano di Tecnologia, Italy)
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Adaptive AI-Driven Material Synthesis: Towards Autonomous 2D Materials Growth
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Gabriel Schleder (Brazilian Nanotechnology National Laboratory (LNNano/CNPEM), Brazil)
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Performance Assessment of Universal Machine Learning Interatomic Potentials: Challenges and Directions for Materials´ Surfaces
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Cormac Toher (The University of Texas at Dallas, USA)
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Predicting the synthesizability and properties of disordered materials by combining first-principles calculations with machine-learning
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Alexander Tyner (NORDITA, Sweden)
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Machine learning guided discovery of spin-resolved topological insulators
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| 15/15 |
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| Orals Parallel Session Seniors |
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Xavier Cetó (Universitat Autònoma de Barcelona, Spain)
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Rapid field identification of illicit drugs based on electroanalysis assisted by machine learning
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Timoteo Colnaghi (Max Planck Computing and Data Facility, Germany)
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The role of AI and ML in the development of a multiscale modeling suite for sustainable magnetic materials
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Stephen Dale (IFIM, Singapore)
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Transferable diversity – a data-driven representation of chemical space
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Gustavo Dalpian (USP, Brazil)
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Learning from machine learning: the case of band-gap directness in semiconductors
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Konrad Eiler (UAB, Spain)
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Guiding experimentalists with machine learning towards optimal Ni-W coatings for fuel cells
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Kavita Joshi (CSIR National Chemical Laboratory, India)
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Solid-state hydrogen storage: Decoding the path through ML guided experiments
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Nikita Kazeev (Institute for Functional Intelligent Materials, National University of Singapore, Singapore)
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WyckoffTransformer: Autoregressive Generation of Crystals
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Mikhail Lazarev (HSE, Russia)
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Symbolic regression for defects interactions in MoS2 and WSe2
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Tianbo Li (SEA AI LAB, Singapore)
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Variational density functional theory using the JAX deep-learning differentiable framework
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Zan Lian (Institute of Chemical Research of Catalonia (ICIQ), Spain)
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Understanding the Dynamic Behavior of Oxide-Derived Copper in CO2 Reduction with Machine Learning Based Large-Scale Simulation
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Ioan Bogdan Magdau (Newcastle University, UK)
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Foundational MLIP: the Li-ion Battery
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Cristiano Malica (University of Bremen, Germany)
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Dynamics of oxidation states in transition metals of Li-ion battery cathodes
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Adithya Nair (L´institut de recherche sur les céramiques (IRCER) , France)
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Convolutional neural network analysis of x-ray diffraction data
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Antonio Pena Corredor (IRT Saint Exupéry, France)
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Physically informed machine learning algorithms for the mastering of additive manufacturing processes
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Pablo Piaggi (CIC nanoGUNE, Spain)
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Understanding crystallization from solution and at interfaces with ab-initio machine-learning models
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Marcin Plodzien (ICFO – The Institute of Photonic Sciences, Spain)
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Attention-based neural networks for Quantum State Tomography
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Elias Polak (University of Fribourg, Switzerland)
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Applying a Well-Defined Energy Density for Machine-Learned Density Functionals
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Jordi Riu Vicente (UPC, Spain)
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Reinforcement Learning based Quantum Circuit Optimization via ZXCalculus
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Andrey Ustyuzhanin (Constructor University, Singapore)
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Towards invertible 2D crystal structure representation for efficient downstream task execution
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| 19/19 |
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| Orals Parallel Session PhDStudents |
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Kevin Alhada-Lahbadi (INSA Lyon, France)
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Ultrafast and accurate prediction of polycrystalline hafnium oxide ferroelectric hysteresis using graph neural networks
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Pol Benítez Colominas (Universitat Politècnica de Catalunya, Spain)
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Predicting thermal effects in optoelectronic properties of solid solutions with crystal graph neural networks
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Junhao Cao (CNRS, France)
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Denoising of 4D-STEM Dataset using Pix2Pix GAN and Artifact Reduction
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Pedro Julián Delgado Galindo (IFMIF-DONES España, Spain)
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Machine Learning Interatomic Potentials for Fusion Oriented Materials
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Pol Febrer (Institut Catala de Nanociencia i Nanotecnologia (ICN2), Spain)
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Learning the density matrix, a symmetry rich encoding of the electron density.
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Aishwaryo Ghosh (S.N. Bose National Centre for Basic Sciences, India)
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Application of machine learning for materials with targeted properties
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Onurcan Kaya (Institut Catala de Nanociencia i Nanotecnologia (ICN2), Spain)
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A Systematic Analysis of Amorphous Boron Nitride Films using Gaussian Approximation Potentials
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Cibrán López Álvarez (Universitat Politècnica de Catalunya, Spain)
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Graph neural networks for prediction of abrupt phase transitions in energy materials: the case of solid-state cooling
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Luis Martin-Encinar (University of Valladolid, Spain)
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A Deep Learning Approach of Surface Elastic Chemical Potential for Accelerating Simulations in Strained Films
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Abhijith S. Parackal (Linköping University, Sweden)
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Inverting unidentified X-ray Powder Diffraction Spectra through Machine Learning-Driven Prototype enumeration
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Ivan Pinto (Institut Catala de Nanociencia i Nanotecnologia (ICN2), Spain)
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Automatic detection of W vacancies in WS2 through CNN
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Sergey Pozdnyakov (EPFL, Switzerland)
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Challenging the dogma of rotational equivariance in atomistic machine
learning
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Egor Shibaev (Constructor University, Germany)
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Non-stoichiometric TMDC rapid energy prediction and stable configuration search
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Anas Siddiqui (University of Warwick, UK)
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Machine-Learned Interatomic Potentials for Transition Metal Dichalcogenide Mo1-xWxS2-2ySe2y Alloys
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Jialiang Tang (University of the Basque Country, Spain)
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Exploring Ground States of Fermi Hubbard Model on Honeycomb Lattices with Counterdiabaticity
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Andrei Tomut (Institut Catala de Nanociencia i Nanotecnologia (ICN2), Spain)
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LatMatcher - AI-Powered Tool for 2D Material Stacking and Property prediction.
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Victor Trinquet (UCLouvain, Belgium)
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Active Learning: Accelerating Discovery of Optimal Optical Materials through Synergistic Computational Approaches
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Ivan Žugec (Centro de Física de Materiales (CSIC-UPV/EHU), Spain)
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Understanding the photoinduced desorption and oxidation of CO on Ru(0001) using a neural network potential energy surface
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| 18/18 |
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| 52/52 |
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