CHEMAI 2025 POSTER SESSION: PART 1

Q. Gao: "Bayesian uncertainty quantification of graph neural networks using stochastic gradient Hamiltonian Monte Carlo"
E. Savino: "Robochem-Flex"
G. Benedini: "Universal Machine Learning Potential for Systems with Reduced Dimensionality"
L. B. Pasca: "Modelling Perovskite Solar Cell Materials Using Machine-Learned Interatomic Potentials"
L. Wu: "Understanding the Structure of Amorphous Na-P Zintl-Phase Battery Anode with ML Interatomic Potentials"
O. L. Kooijman: "Automated DFT and Machine Learning to discover intrinsically recyclable polymers"
G. Li: "Structure-Activity Relationship of Transition Metal Carbide for Hydrodeoxygenation Reaction"
A. Villegas-Morcillo: "All-Atom Protein Sequence Design using Discrete Diffusion Models"
E. Eberhard: "Learning Equivariant Non-local Electron Density Functionals"
E. Kempkes: "A Bayesian Approach to Exploreand Exploit Molecular Free-Energy Landscapes"
F. Wang: "Constrained Composite Bayesian Optimization for Rational Synthesis of Polymeric Particles"
G. Vogel: "Polymer-JEPA: Joint Embedding Predictive Architecture for Self-supervised Pretraining on Polymer Molecular Graphs"
I. Can Oguz: "ML-Accelerated Discovery of Bimetallic HER Catalysts"
J. Hoffmann: "PLASTIC-JUNC: "Predicting nanoPLASTIC Risks to JUNCtional Protein Integrity"
M. Petković: "Automating Materials Simulations with AI Agents"
M. Dieperink: "Optical Tomography: Optical 3D nm-resolved morphology monitoring of nanoparticles in action"
M. Falah: "Using AI to Predict Molecules from Mass Spectra"
A. A. Panahi: "Simulation Acceleration with AI"
T. van Heesch: "Learning the Hill_Climber's Guide for Traversing Free-Energy Barriers in Molecular Dynamics"
A. Böser: "BrickSDLab - A low-cost self-driving lab platform made from LEGO® bricks for rapid prototyping and education"
D. Baum: "Accurate and Affordable Machine Learning for qsGQ through Transfer Learning"
J. Dijkman: "Machine Learning for Classical Density Functional Theory"

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