Infinite dilution activity coefficient is a key thermodynamic parameter in solvent design for chemical processes. Although ...

In recent years, the integration of machine learning and robotics technologies in chemical analysis has transformed the landscape of scientific research and industry practices. This revolution is not ...

A web application for use by experimental chemists created by us. Uploading a file calculated with commercially available software, and the electronic state can be analyzed. We are working on creating ...

Researchers have used machine learning to create a model that simulates reactive processes in organic materials and conditions. Researchers from Carnegie Mellon University and Los Alamos National ...

Chemists have created a machine learning tool that can identify the chemical composition of dried salt solutions from an image with 99% accuracy. By using robotics to prepare thousands of samples and ...

View All Webinars > How SABIC Uses Hybrid Modeling and Machine Learning to Drive Smarter Operations Decisions FREE \| April 29, 2026. REGISTER. Process engineers are under pressure ...

Scanning electron microscopy image (left) shows the surface of a porous asymmetric UF membrane created at Cornell by mixing chemically distinct block copolymer micelles. Machine-learning segmentation ...

The increasing complexity of modern chemical engineering processes presents significant challenges for timely and accurate anomaly detection. Traditional ...

Machine learning is a subfield of artificial intelligence, which explores how to computationally simulate (or surpass) humanlike intelligence. While some AI techniques (such as expert systems) use ...

(Nanowerk News) Researchers from Carnegie Mellon University and Los Alamos National Laboratory have used machine learning to create a model that can simulate reactive processes in a diverse set of ...