Uncertainty quantification in atomistic modeling: From uncertainty-aware density functional theory to machine learning 2025
תיאור
The event titled 'Uncertainty Quantification in Atomistic Modeling: From Uncertainty-Aware Density Functional Theory to Machine Learning' will take place at CECAM-HQ in Lausanne, Switzerland, from November 25 to November 28, 2025. This gathering will bring together leading researchers and practitioners in the fields of computational materials science and machine learning to explore the critical role of uncertainty quantification in atomic-scale modeling. Participants will engage in discussions on the latest advancements in uncertainty-aware density functional theory and its integration with machine learning techniques.
נקודות עיקריות
Focus on uncertainty quantification in atomistic modeling. Integration of density functional theory with machine learning. Discussions on error propagation in multiscale simulations. Exploration of uncertainty-aware density functional theory. Advancements in machine learning applications for materials science. Strategies for error balancing in DFT simulations. Development of uncertainty estimation methods in atomistic ML. Applications of UQ in atomistic modeling. Active learning strategies for robust dataset construction. Collaborative discussions shaping the future of UQ in atomistic modeling.
מיקום האירוע
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שתף אירוע זה כדי להתחיל שיחות, להזמין עמיתים ולהתחבר לפני תחילתו.
