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Generative modeling enables molecular structure retrieval from Coulomb explosion imaging
Capturing the structural changes that molecules undergo during chemical reactions in real space and time is a long-standing dream and an essential prerequisite for understanding and ultimately controlling femtochemistry. A key approach to tackle this challenging task is Coulomb explosion imaging, which benefited decisively from recently emerging high-repetition-rate X-ray free-electron laser sources. With this technique, information on the molecular structure is inferred from the momentum distributions of the ions produced by the rapid Coulomb explosion of molecules. Retrieving molecular structures from these distributions poses a highly non-linear inverse problem that remains unsolved for molecules consisting of more than a few atoms. Here, we address this challenge using a diffusion-based Transformer neural network. We show that the network reconstructs unknown molecular geometries from ion-momentum distributions with a mean absolute error below one Bohr radius, which is half the length of a typical chemical bond.
Discovering mechanisms for materials microstructure optimization via reinforcement learning of a generative model
Abstract The design of materials structure for optimizing functional properties and potentially, the discovery of novel behaviors is a keystone problem in materials science. In many cases microstructural models underpinning materials functionality are available and well understood. However, optimization of average properties via microstructural engineering often leads to combinatorically intractable problems. Here, we explore the use of the reinforcement learning (RL) for microstructure optimization targeting the discovery of the physical mechanisms behind enhanced functionalities. We illustrate that RL can provide insights into the mechanisms driving properties of interest in a 2D discrete Landau ferroelectrics simulator. Intriguingly, we find that non-trivial phenomena emerge if the rewards are assigned to favor physically impossible tasks, which we illustrate through rewarding RL agents to rotate polarization vectors to energetically unfavorable positions. We further find that strategies to induce polarization curl can be non-intuitive, based on analysis of learned agent policies. This study suggests that RL is a promising machine learning method for material design optimization tasks, and for better understanding the dynamics of microstructural simulations.
Score-based generative models for calorimeter shower simulation
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Evaluating generative models in high energy physics
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Toward a generative modeling analysis of CLAS exclusive 2 π photoproduction
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Point cloud approach to generative modeling for galaxy surveys at the field level
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Flowing Through Hilbert Space: Quantum-Enhanced Generative Models for Lattice Field Theory
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Potential Flow Generator With L 2 Optimal Transport Regularity for Generative Models
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Intra-class data augmentation with deep generative models of threat objects in baggage radiographs
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Conditional Sampling with Monotone GANs: From Generative Models to Likelihood-Free Inference
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Intra-class data augmentation with deep generative models of threat objects in baggage radiographs
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Big Microstructure Datasets for Materials Informatics: Using Statistically Conditioned Generative Models to Curate Big Datasets
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Designing Large Datasets: Data-Scarce and Stable Deep Generative Models for Turning Sparse Experiments into Big Datasets in Materials Science
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Big Microstructure Datasets for Materials Informatics: Using Statistically Conditioned Generative Models to Curate Big Datasets
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