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Wed 03 |
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Thu 04 |
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Chairperson: Bingqing Cheng |
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09:00-09:20 |
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INVITED |
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A foundational atomistic model for materials |
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Gabor Csanyi,
University of Cambridge, UK |
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09:20-09:40 |
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INVITED |
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Machine learning electrochemistry |
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Nicola Marzari,
EPFL, Switzerland |
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09:40-10:00 |
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INVITED |
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Multiscale machine learning: from quantum chemistry to dislocation dynamics |
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Boris Kozinsky,
Harvard University, USA |
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10:00-10:30 |
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KEYNOTE |
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Beyond Crystallinity and Throughput: Machine Learning Accelerated Materials Discovery for Energy Conversion and Storage |
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Karsten Reuter,
Max-Planck-Gesellschaft, Germany |
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10:30-11:00 |
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Coffee Break / Poster Session / Exhibition |
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11:00-12:00 |
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IoP Publishing Round Table discussion on opportunities and challenges in AI for Advanced Materials
Panel members: Amanda Barnard / Boris Kozinsky / Anatole von Lilienfeld / Silvana Botti |
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Chairperson: Stephan Roche |
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Innovation Landscape for AI4AM |
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12:00-12:15 |
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INVITED |
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Platform MaterialDigital – enabling the industrial material data space of the future |
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Wolfgang Wenzel,
Karlsruhe Institute of Technology, Germany |
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12:15-12:30 |
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INVITED |
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The Innovative Advanced Materials Initiative and the Innovative Advanced Materials for Europe partnership |
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Andrea Ferrari,
Cambridge Graphene Centre / University of Cambridge, UK |
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12:30-12:40 |
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INVITED |
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Industry as the relevant driving force of scientific developments |
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Kostya Novoselov,
NUS, Singapore |
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12:40-13:15 |
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Round Table Future of Materials: Science, Technology and Solutions Panel members: Andrea Ferrari / Kostya Novoselov / Stephan Roche / Andrey Ustyuzhanin and Laurent Dedenis
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13:15-14:00 |
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Cocktail Lunch (offered by AI4AM2024 organisers) |
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14:00-14:30 |
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Poster Session 2 / Exhibition |
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14:00-14:30 |
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Machine Intelligence // Workshop
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We invite you to attend our hands-on session, where we will guide you through a comprehensive data science project using a sample dataset and provide valuable insights and tools to elevate your research capabilities.
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You will gain practical experience with Initiating a Data Science Project, Data Wrangling with Python, Exploratory Data Analysis and Visualization, Machine Learning Model Training.
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After the workshop you will have a chance to get a certificate of completion if you register on Constructor Platform and hand-in your assignments.
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Ekaterina Butyugina (Constructor Academy, Switzerland)
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Parallel Session - PhD Students - I (AULA MAGNA) |
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Chairperson: Nicola Marzari |
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14:30-14:40 |
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Application of machine learning for materials with targeted properties |
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Aishwaryo Ghosh,
S.N. Bose National Centre for Basic Sciences, India |
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14:40-14:50 |
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A Deep Learning Approach of Surface Elastic Chemical Potential for Accelerating Simulations in Strained Films |
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Luis Martin-Encinar,
University of Valladolid, Spain |
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14:50-15:00 |
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Challenging the dogma of rotational equivariance in atomistic machine
learning |
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Sergey Pozdnyakov,
EPFL, Switzerland |
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15:00-15:10 |
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Learning the density matrix, a symmetry rich encoding of the electron density. |
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Pol Febrer,
Institut Catala de Nanociencia i Nanotecnologia (ICN2), Spain |
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15:10-15:20 |
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LatMatcher - AI-Powered Tool for 2D Material Stacking and Property prediction. |
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Andrei Tomut,
Institut Catala de Nanociencia i Nanotecnologia (ICN2), Spain |
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15:20-15:30 |
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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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Ivan Žugec,
Centro de Física de Materiales (CSIC-UPV/EHU), Spain |
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15:30-15:40 |
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Automatic detection of W vacancies in WS2 through CNN |
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Ivan Pinto,
Institut Catala de Nanociencia i Nanotecnologia (ICN2), Spain |
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15:40-15:50 |
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Machine Learning Interatomic Potentials for Fusion Oriented Materials |
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Pedro Julián Delgado Galindo,
IFMIF-DONES España, Spain |
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Parallel Session - PhD Students - II (Room 011+013) |
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Chairperson: Gabor Csanyi |
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14:30-14:40 |
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Ultrafast and accurate prediction of polycrystalline hafnium oxide ferroelectric hysteresis using graph neural networks |
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Kevin Alhada-Lahbadi,
INSA Lyon, France |
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14:40-14:50 |
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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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Cibrán López Álvarez,
Universitat Politècnica de Catalunya, Spain |
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14:50-15:00 |
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Predicting thermal effects in optoelectronic properties of solid solutions with crystal graph neural networks |
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Pol Benítez Colominas,
Universitat Politècnica de Catalunya, Spain |
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15:00-15:10 |
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Machine-Learned Interatomic Potentials for Transition Metal Dichalcogenide Mo1-xWxS2-2ySe2y Alloys |
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Anas Siddiqui,
University of Warwick, UK |
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15:10-15:20 |
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A Systematic Analysis of Amorphous Boron Nitride Films using Gaussian Approximation Potentials |
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Onurcan Kaya,
Institut Catala de Nanociencia i Nanotecnologia (ICN2), Spain |
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15:20-15:30 |
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Non-stoichiometric TMDC rapid energy prediction and stable configuration search |
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Egor Shibaev,
Constructor University, Germany |
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15:30-15:40 |
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Exploring Ground States of Fermi Hubbard Model on Honeycomb Lattices with Counterdiabaticity |
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Jialiang Tang,
University of the Basque Country, Spain |
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15:40-15:50 |
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Active Learning: Accelerating Discovery of Optimal Optical Materials through Synergistic Computational Approaches |
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Victor Trinquet,
UCLouvain, Belgium |
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15:50-16:00 |
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Denoising of 4D-STEM Dataset using Pix2Pix GAN and Artifact Reduction |
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Junhao Cao,
CNRS, France |
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16:00-16:30 |
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Coffee Break / Poster Session / Exhibition |
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Parallel Session - Seniors I (AULA MAGNA) |
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Chairperson: Boris Kozinsky |
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16:30-16:45 |
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Solid-state hydrogen storage: Decoding the path through ML guided experiments |
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Kavita Joshi,
CSIR National Chemical Laboratory, India |
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16:45-17:00 |
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Learning from machine learning: the case of band-gap directness in semiconductors |
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Gustavo Dalpian,
USP, Brazil |
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17:00-17:15 |
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Dynamics of oxidation states in transition metals of Li-ion battery cathodes |
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Cristiano Malica,
University of Bremen, Germany |
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17:15-17:30 |
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Rapid field identification of illicit drugs based on electroanalysis assisted by machine learning |
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Xavier Cetó,
Universitat Autònoma de Barcelona, Spain |
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17:30-17:45 |
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Applying a Well-Defined Energy Density for Machine-Learned Density Functionals |
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Elias Polak,
University of Fribourg, Switzerland |
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17:45-18:00 |
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Towards invertible 2D crystal structure representation for efficient downstream task execution |
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Andrey Ustyuzhanin,
Constructor University, Singapore |
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18:00-18:15 |
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WyckoffTransformer: Autoregressive Generation of Crystals |
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Nikita Kazeev,
Institute for Functional Intelligent Materials, National University of Singapore, Singapore |
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18:15-18:30 |
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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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Zan Lian,
Institute of Chemical Research of Catalonia (ICIQ), Spain |
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18:30-18:45 |
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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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Timoteo Colnaghi,
Max Planck Computing and Data Facility, Germany |
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18:45-19:00 |
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Guiding experimentalists with machine learning towards optimal Ni-W coatings for fuel cells |
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Konrad Eiler,
UAB, Spain |
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Parallel Session - Seniors II (Room 011+013) |
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Chairperson: Volker Deringer |
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16:30-16:45 |
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Physically informed machine learning algorithms for the mastering of additive manufacturing processes |
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Antonio Pena Corredor,
IRT Saint Exupéry, France |
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16:45-17:00 |
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Foundational MLIP: the Li-ion Battery |
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Ioan Bogdan Magdau,
Newcastle University, UK |
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17:00-17:15 |
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Understanding crystallization from solution and at interfaces with ab-initio machine-learning models |
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Pablo Piaggi,
CIC nanoGUNE, Spain |
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17:15-17:30 |
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Variational density functional theory using the JAX deep-learning differentiable framework |
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Tianbo Li,
SEA AI LAB, Singapore |
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17:30-17:45 |
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Transferable diversity – a data-driven representation of chemical space |
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Stephen Dale,
IFIM, Singapore |
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17:45-18:00 |
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Convolutional neural network analysis of x-ray diffraction data |
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Adithya Nair,
L´institut de recherche sur les céramiques (IRCER) , France |
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18:00-18:15 |
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Reinforcement Learning based Quantum Circuit Optimization via ZXCalculus |
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Jordi Riu Vicente,
UPC, Spain |
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18:15-18:30 |
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Symbolic regression for defects interactions in MoS2 and WSe2 |
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Mikhail Lazarev,
HSE, Russia |
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18:30-18:45 |
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Attention-based neural networks for Quantum State Tomography |
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Marcin Plodzien,
ICFO – The Institute of Photonic Sciences, Spain |
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