Finding high-performing catalysts, which are used to accelerate processes from chemical manufacturing to energy production, ...

Software simulates 370,000 steps in under 100 hours, potentially cutting demand for time on supercomputers by orders of ...

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 ...

This presentation will focus on how machine learning is assisting in solving process chemistry challenges. Multiple use-cases will be presented, along with an overview of emerging techniques. The ...

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 ...

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

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 ...

A team of researchers has used machine learning to design a new stainless steel alloy built specifically for 3D printing, one ...

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 ...