The Department of Industrial Engineering and Management Sciences is a leader in the science of decision-making in complex environments through innovation in algorithms, computation, and mathematical ...
Researchers at the Indian Institute of Science (IISc), with collaborators at University College London, have developed machine learning-based methods to predict material properties even with limited ...
Electron density prediction for a four-million-atom aluminum system using machine learning, deemed to be infeasible using traditional DFT method. × Researchers from Michigan Tech and the University of ...
A fast and accurate surrogate model screens over 10,000 possible metal-oxide supports for a platinum nanocatalyst to prevent sintering under high temperatures.
Penn State researchers created seven new high-entropy oxides by removing oxygen during synthesis, enabling metals that normally destabilize to form rock-salt ceramics. Machine learning helped identify ...
Perovskites are a class of materials with great potential as solar cells. UC Davis materials scientists have used machine learning to explore the wide variety of perovskite formulas to find those best ...
Research shows how artificial intelligence is revolutionizing plastics manufacturing through material development and process ...
Artificial Intelligence and its related tools, such as machine learning, deep learning, and neural networks, are revolutionizing every field of life. The domain of materials science and engineering is ...
Every time Martin publishes a story, you’ll get an alert straight to your inbox! Enter your email By clicking “Sign up”, you agree to receive emails from ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results
Feedback