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Showing 1 - 7 results of 7 for search '(((( algorithm python function ) OR ( algorithm both function ))) OR ( algorithm where function ))~', query time: 0.40s Refine Results
  1. 1
    An expectation-maximization algorithm for finding noninvadable stationary states.

    An expectation-maximization algorithm for finding noninvadable stationary states. by Robert Marsland (8616483)

    Published 2020
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  2. 2
    Comparison of scores obtained by our interpenetration and scoring algorithm (ISA) and ROSETTA for a subset of structures.

    Comparison of scores obtained by our interpenetration and scoring algorithm (ISA) and ROSETTA for a subset of structures. by Kevin Sawade (16726527)

    Published 2023
    “…However, our algorithm was 1000 times faster than pyROSETTA (both algorithms have been parallelized on a per-structure basis using the Python package joblib [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1010531#pcbi.1010531.ref069" target="_blank">69</a>]).…”
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  3. 3
    Data for "A multi-objective platform for autonomous property targeting and optimization of colloidal lead halide perovskite quantum dots"

    Data for "A multi-objective platform for autonomous property targeting and optimization of colloidal lead halide perovskite quantum dots" by Neal Munyebvu (19690954)

    Published 2025
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  4. 4
    Spatiotemporal Soil Erosion Dataset for the Yarlung Tsangpo River Basin (1990–2100)

    Spatiotemporal Soil Erosion Dataset for the Yarlung Tsangpo River Basin (1990–2100) by peng xin (21382394)

    Published 2025
    “…Additionally, a Genetic Algorithm (GA) was applied in each iteration to optimize the hyperparameters of the XGBoost model, which is crucial for enhancing both the efficiency and robustness of the model (Zhong and Liu, 2024; Zou et al., 2024). …”
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  5. 5
    Landscape17

    Landscape17 by Vlad Carare (22092515)

    Published 2025
    “…This dataset features global potential energy surface representations generated using the energy landscape framework and includes regions crucial for accurately reproducing both thermodynamic and kinetic properties. For each of the selected six molecules (ethanol, malonaldehyde, paracetamol, salicylic acid, azobenzene, and aspirin) we provide all the minima and transition states, along with configurations from the two approximate steepest-descent paths connecting each transition state to the corresponding minima, computed using hybrid-level density functional theory. …”
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  6. 6
    Code

    Code by Baoqiang Chen (21099509)

    Published 2025
    “…We implemented machine learning algorithms using the following R packages: rpart for Decision Trees, gbm for Gradient Boosting Machines (GBM), ranger for Random Forests, the glm function for Generalized Linear Models (GLM), and xgboost for Extreme Gradient Boosting (XGB). …”
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  7. 7
    Core data

    Core data by Baoqiang Chen (21099509)

    Published 2025
    “…We implemented machine learning algorithms using the following R packages: rpart for Decision Trees, gbm for Gradient Boosting Machines (GBM), ranger for Random Forests, the glm function for Generalized Linear Models (GLM), and xgboost for Extreme Gradient Boosting (XGB). …”
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