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

Recent advancements in machine learning have substantially transformed the optimisation of the steelmaking process. Traditional methods, often limited by complex thermodynamic interactions and ...

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 ...

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 ...

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 ...

Machine learning model provides quick method for determining the composition of solid chemical mixtures using only photographs of the sample. Machine learning model provides quick method for ...

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 ...

Validating drug production processes need not be a headache, according to AI researchers, who say machine learning could be a single answer to biopharma’s multivariate problem. The FDA defines process ...

(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 ...