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

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

"…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>]).…"

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

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

"…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). …"

Landscape17 حسب Vlad Carare (22092515)

"…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. …"

Code حسب Baoqiang Chen (21099509)

"…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). …"

Core data حسب Baoqiang Chen (21099509)

"…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). …"