A new study presents a machine learning model that accurately predicts the compressive strength of high-strength concrete, ...

Scikit-learn, PyTorch, and TensorFlow remain core tools for structured data and deep learning tasks.New libraries like JAX, ...

Key Takeaways The transition requires upskilling in Python, statistics, and machine learning.Practical experience with ...

Clustering can be done using various algorithms such as k-means, hierarchical clustering, density-based spatial clustering of applications with noise (DBSCAN) and Gaussian mixture model (GMM) ...

A python code was developed to prepare the data for machine learning by collecting and formatting oxidation rate constants, alloy compositions and environment of exposure into a single data frame.

A new study introduced a machine learning-powered clustering model that incorporates both basic features and target properties, successfully grouping over 1,000 inorganic materials.

Conventional clustering techniques often focus on basic features like crystal structure and elemental composition, neglecting target properties such as band gaps and dielectric constants. A Tokyo Tech ...

Cluster analysis, a commonly used machine-learning technique uses these basic features to not only categorize materials and summarize similarities between them but also provide information ...