Back in the old days—the really old days—the task of designing materials was laborious. Investigators, over the course of 1,000-plus years, tried to make gold by combining things like lead, mercury, ...
Metal–organic frameworks (MOFs) are highly porous and versatile materials studied extensively for applications such as carbon capture and water harvesting. However, computing phonon-mediated ...
Accurate prediction of shallow donor electron binding energies is critical for device modeling, dopant activation, and donor-based quantum technologies. Traditional beyond-DFT approaches are ...
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 ...
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 ...
Researchers present a comprehensive review of frontier AI applications in computational structural analysis from 2020 to 2025, focusing on graph neural networks (GNNs), sequence-to-sequence (Seq2Seq) ...
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 ...
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New atom-level calculations show transistors could shrink below 4 nanometers
KAIST researchers have developed a simulation-based method to predict how small future transistors can ...
CDVAE, a symmetry-aware generative AI framework that embeds space-group information into the generation of crystal structures ...
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