EMBL-EBI scientists and collaborators at Heidelberg University have developed CORNETO, a new computational tool that uses ...

Machine learning helps improve accuracy and efficiency of small-molecule calculations Microsoft researchers used deep learning to create new DFT model by Sam Lemonick, special to C&EN June 20, 2025 ...

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) ...

Federated Learning (FL) has emerged as a promising framework to address data privacy concerns associated with mobile devices, in contrast to conventional Machine Learning (ML). However, traditional FL ...

Typical Azure Machine Learning Project Lifecycle (source: Microsoft). At the upcoming Visual Studio Live! @ Microsoft HQ 2025 conference in Redmond, Eric D. Boyd, founder and CEO of responsiveX, will ...

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.

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 ...

Lemke and her team leverage Azure Machine Learning and its associated Machine Learning Operations solutions to develop, provide workflows, implement deep learning algorithms, and monitor and automate ...

The unsupervised machine learning is presented as a powerful tool for clustering application in order to recognize sag, swell and interruption for power quality applications. The K-means clustering ...