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

Landscape17 حسب Vlad Carare (22092515)

"…<h2>Overview</h2><p dir="ltr">Machine learning interatomic potentials (MLIPs) have achieved remarkable accuracy on standard benchmarks, yet their ability to reproduce molecular kinetics – critical for reaction rate calculations – remains largely unexplored. …"

Predictive Analysis of Mushroom Toxicity Based Exclusively on Their Natural Habitat. حسب Enrico Bertozzi (22461709)

"…The analysis was conducted in a Jupyter Notebook environment, using Python and libraries such as Scikit-learn and Pandas. …"

Brain-in-the-Loop Learning for Intelligent Vehicle Decision-Making حسب Xiaofei Zhang (16483224)

"…To achieve policy learning within limited BiTL training periods, we add two modification features to the proposed algorithm based on TD3. …"

Code حسب Baoqiang Chen (21099509)

"…We divided the dataset into training and test sets, using 70% of the genes for training and 30% for testing. 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 divided the dataset into training and test sets, using 70% of the genes for training and 30% for testing. 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). …"