Until now, designing complex metamaterials with specific mechanical properties required large and costly experimental and simulation datasets. The method enables ...
Materials with advanced customized properties drive innovation in a number of real-life applications across various fields, such as information technology, transportation, green energy and health ...
An academia-industry collaboration developed a new sampling algorithm for Design of Experiment intending to democratize experimental design.
5don MSN
Machine learning workflow enables faster, more reliable organic crystal structure prediction
Prediction of crystal structures of organic molecules is a critical task in many industries, especially in pharmaceuticals ...
The era has arrived in which artificial intelligence (AI) autonomously imagines and predicts the structures and properties of ...
The field of computational materials science has been profoundly transformed by integrating deep learning and other machine learning methodologies. These sophisticated data-driven approaches have ...
Machine learning, a key enabler of artificial intelligence, is increasingly used for applications like self-driving cars, ...
Brian Spears and colleagues built a generative machine learning model that was used to successfully predict the outcome of a recent fusion ignition experiment at the U.S. National Ignition Facility ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results
Feedback