Implementing machine learning tools in wastewater treatment plants offers measurable benefits such as chemical cost ...

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 organic materials ...

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 ...

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 ...

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 ...

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 ...

Drug discovery is like molecular Tetris. Chemists snap atoms together, adjusting the pieces until everything fits and suddenly, a molecule makes a promising new medicine. Normally, creating better ...

Adejare Adesiyan’s engineering work and research span three countries — Nigeria, South Africa, and the United States. He is currently a mechanical engineering master’s student at Iowa State University ...

A simulation demonstrates the reactions that the ANI-1xnr can produce. ANI-1xnr can simulate reactive processes for organic materials, such as as carbon, using less computing power and time than ...

(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 ...