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1. Geometric instability catalyzes mitochondrial fission

   https://doi.org/10.1091/mbc.E18-01-0018  

   Irajizad, Ehsan; Ramachandran, Rajesh; Agrawal, Ashutosh; Bassereau, Patricía (January 2019, Molecular Biology of the Cell)

   The mitochondrial membrane undergoes extreme remodeling during fission. While a few membrane-squeezing proteins are recognized as the key drivers of fission, there is a growing body of evidence that strongly suggests that conical lipids play a critical role in regulating mitochondrial morphology and fission. However, the mechanisms by which proteins and lipids cooperate to execute fission have not been quantitatively investigated. Here, we computationally model the squeezing of the largely tubular mitochondrion and show that proteins and conical lipids can act synergistically to trigger buckling instability and achieve extreme constriction. More remarkably, the study reveals that the conical lipids can act with different fission proteins to induce hierarchical instabilities and create increasingly narrow and stable constrictions. We reason that this geometric plasticity imparts significant robustness to the fission reaction by arresting the elastic tendency of the membrane to rebound during protein polymerization and depolymerization cycles. Our in vitro study validates protein–lipid cooperativity in constricting membrane tubules. Overall, our work presents a general mechanism for achieving drastic topological remodeling in cellular membranes. 

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2. Energy competition and pairing effect for the fission path with a microscopic model

   https://doi.org/10.3389/fphy.2022.986488  

   Fujio, Kazuki; Ebata, Shuichiro; Inakura, Tsunenori; Ishizuka, Chikako; Chiba, Satoshi (September 2022, Frontiers in Physics)

   We studied the fission barrier of 236 U with a microscopic mean-field model employing Skyrme-type effective interaction. It has been known that the microscopic mean-field calculation had a trend of overestimating the fission barriers derived from the fission cross section, and our results were found to be in accord with it. To reveal a major factor of the discrepancy, we investigated various components of the Skyrme energy-density functional building of the fission barrier height by a static mean-field model, including nuclear pairing correlation. We found that the spin-orbit and pairing terms affected the fine structure of the fission barrier as a function of elongation of the nucleus. Therefore, we investigated the sensitivity of the fission barrier height on the pairing strength, considering the change of level density along the calculated fission path. 

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3. Nuclear fission properties of super heavy nuclei described within the four-dimensional Langevin model

   https://doi.org/10.3389/fphy.2023.1111868  

   Ishizuka, Chikako; Zhang, Xuan; Shimada, Kazuya; Usang, Mark; Ivanyuk, Fedir; Chiba, Satoshi (January 2023, Frontiers in Physics)

   Understanding of fission properties of super-heavy nuclei (SHN) is essential not only for the synthesis of new elements but also for astrophysical nucleosynthesis because fission fragments from SHN are recycled as the seed nuclei of the r-process. A recent discovery of the r-process site by the gravitational wave observations requires more precise nuclear information for the detailed simulation of the r-process nucleosynthesis. However, the fission mechanisms of the SHN are not understood well, and therefore theoretical predictions of distributions of the fission fragments of SHN are very model-dependent. Our four-dimensional Langevin model can calculate various properties of the fission fragments, such as the distribution of fission yields, kinetic energies, and deformation of fission fragments and their correlations just after scission. Those results are consistent with the experimental data, especially in the actinide region without adjusting parameters. Based on such a reliable model, we previously investigated the fission of representative SHN where the experimental data exist and found that doubly-magic shell closure of 132 Sn and 208 Pb dominates the fission process. This paper demonstrates the results of our calculations for the systematics of fission yield and the total kinetic energies from the neutron-rich to the neutron-deficient side of SHN. We also show decomposition of fission modes, such as standard/super-long/super-short modes, based on a Brosa-like concept. 

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4. Point-cloud based machine learning for classifying rare events in the Active-Target Time Projection Chamber

   https://doi.org/10.1016/j.nima.2024.170002  

   Dey, Poulomi; Anthony, Adam K; Hunt, Curtis; Kuchera, Michelle P; Ramanujan, Raghuram; Lynch, William G; Tsang, ManYee Betty; Wieske, Joseph M; Ajongbah, Jessica W; Beceiro-Novo, Saul; et al (March 2025, Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment)

   In this work, we assess the use of machine learning to classify fission events in the Active Target Time Projection Chamber (AT-TPC) using data from an experiment performed at the National Superconducting Cyclotron Laboratory at Michigan State University. The experiment produces an extremely large quantity of data, less than 3% of which are fission events. Therefore, separating fission events from the background beam events is critical to an efficient analysis. A heuristic method was developed to classify events as Fission or Non-Fission based on hand-tuned parameters. However, this heuristic method places 5% of all events into an Unlabeled category, including 15% of all fission events. We present a PointNet model trained on the data labeled by the heuristic method. This model is then used to generate labels for the events in the Unlabeled category. Using the heuristic and machine learning methods together, we can successfully identify 99% of fission events. 

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5. Machine learning photodynamics decode multiple singlet fission channels in pentacene crystal

   https://doi.org/10.1038/s41467-025-56480-y  

   Li, Zhendong; Hernández, Federico_J; Salguero, Christian; Lopez, Steven_A; Crespo-Otero, Rachel; Li, Jingbai (January 2025, Nature Communications)

   Abstract Crystalline pentacene is a model solid-state light-harvesting material because its quantum efficiencies exceed 100% via ultrafast singlet fission. The singlet fission mechanism in pentacene crystals is disputed due to insufficient electronic information in time-resolved experiments and intractable quantum mechanical calculations for simulating realistic crystal dynamics. Here we combine a multiscale multiconfigurational approach and machine learning photodynamics to understand competing singlet fission mechanisms in crystalline pentacene. Our simulations reveal coexisting charge-transfer-mediated and coherent mechanisms via the competing channels in the herringbone and parallel dimers. The predicted singlet fission time constants (61 and 33 fs) are in excellent agreement with experiments (78 and 35 fs). The trajectories highlight the essential role of intermolecular stretching between monomers in generating the multi-exciton state and explain the anisotropic phenomenon. The machine-learning-photodynamics resolved the elusive interplay between electronic structure and vibrational relations, enabling fully atomistic excited-state dynamics with multiconfigurational quantum mechanical quality for crystalline pentacene. 

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