The arrangement of electrons in matter, known as the electronic structure, plays a crucial role in fundamental but also applied research such as drug design and energy storage. However, the lack of a ...
Hosted on MSN
Predicting material failure: Machine learning spots early abnormal grain growth signs for safer designs
A team of Lehigh University researchers has successfully predicted abnormal grain growth in simulated polycrystalline materials for the first time—a development that could lead to the creation of ...
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 ...
Imagine having a super-powered lens that uncovers hidden secrets of ultra-thin materials used in our gadgets. Research led by University of Florida engineering professor Megan Butala enables a novel ...
An overview of fatigue failure and how it affects mechanical systems. How FEA software enables engineers to predict fatigue failure points on structural designs, before the manufacturing process. From ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results
Feedback