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Scientific Poster Session: An afternoon of discussions and exchange
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The event featured scientific posters from research groups representing five departments across four TUM Schools (School of Computation, Information and Technology, School of Engineering and Design, School of Life Sciences, School of Natural Sciences), highlighting the breadth of research within the AMC community. The poster session provided an opportunity for researchers at different career stages to present their latest work, engage in scientific discussions, and explore potential collaborations.
The presented research covered a wide range of topics in atomistic modeling and computational science, including molecular dynamics simulations, machine learning and foundation models for molecular systems, computational materials science, and biomolecular modeling. Posters addressed applications such as perovskite solar cells, solid electrolytes, organic semiconductors, and olfactory receptors, illustrating the diversity of approaches and scientific questions pursued within the center.
Accompanied by snacks and refreshments, the informal setting encouraged lively discussions among researchers from different disciplines. By bringing together expertise from across TUM, the Poster Sessionfostered scientific exchange and created opportunities for new interdisciplinary collaborations within the AMC research community.
The event featured the following scientific posters:
Nitik Bhatia: MACE4IRmol: An Uncertainty-Aware Foundation Model for Molecular Infrared Spectroscopy (Prof. Patrick Rinke)
Florian Bößl: A Large-Scale Molecular Dynamics Resource for Exploring the Odorant Receptor Conformational Landscape (Prof. Antonella Di Pizio)
Nuno Costa: Morphology-Aware Peptide Discovery via Masked Conditional Generative Modeling (Prof. Julija Zavadlav)
Alexey Gudovannyy: Modeling the Ionization Potential and Electron Affinity in Organic Semiconductors(Prof. Frank Ortmann)
Inigo Iribarren: A Computational Workflow to Unravel the Structural Dynamic of Supramolecular Metallacages in Solution (Prof. Alessio Gagliardi)
Takeru Miyagawa: Accuracy of MACE Foundation Model Potentials for Solid Electrolytes (Prof. David Egger)
Prajwal Pisal: Uncovering Facet-Controlled Activity and Selectivity in CO2 Hydrogenation with Machine Learned Force Fields (Prof. Patrick Rinke)
Niccolo Radice: From In-Situ Photoluminescence to Device Physics: A Deep Learning Framework for Perovskite Solar Cell Characterization (Prof. Alessio Gagliardi)
Clarissa Rienäcker: MD and Pocketome Analysis of Olfactory Receptors Illustrate Ligand-based Conformational Differences (Prof. Antonella Di Pizio)
Martin Schwade: How to Train Your Hamster: Learning an Effective Hamiltonian at Finite Temperatures (Prof. David Egger)
Philip Schwinghammer: Analysis of the Interplay Between Electronic Structure and Lattice Symmetry in Bismuth Vanadate (Prof. David Egger)
Ian Störmer: Graph Neural Network Potentials for Million-Atom Molecular Dynamics Simulations of Aluminum Solidification (Prof. Julija Zavadlav)
Nisarg Trivedi: Wannier-Orbital Transient Polaron Localisation for Electron and Spin Transport in Organic Semiconductors (Prof. Frank Ortmann)
Linying Zhang: ConSolv: Solvent-Conditional Machine Learning Implicit Solvent Potential (Prof. Julija Zavadlav)
