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O-kRystal, a periodic E(3)-equivariant graph neural network, learns corrections to electronic states beyond DFT, achieving HSE06 band predictions across nearly 6,000 materials, G0W0 quasiparticle energies in 2D systems, and a 68.7% reduction in band energy MAE on strongly correlated materials.
Researchers show that DFT-relaxed structural distortions cause machine-learning screens to misclassify hidden altermagnets as antiferromagnets, and propose a tolerance-aware symmetry pipeline that discovers six new altermagnet materials including Ba₂FeMoO₆ and Sr₂FeWO₆, with spin splitting of 85–405 meV at zero net magnetization.
Researchers introduce AI Theorist, a multi-agent AI system that autonomously develops physical models through hypothesis generation and first-principles calculations, and apply it to reveal excitonic structure in α-RuCl3 from optical spectra. This is claimed to be the first demonstration of an AI system autonomously creating a physical model to explain unpublished experimental observations in a quantum material.
A Show HN post highlighting KCC (Kindle Comic Converter), an open-source tool that maximizes manga/comic image quality on e-ink ereaders using techniques like 2D DFT to fix rainbow moiré effects, black-level correction, dithering, and device-native downscaling for fullscreen display without margins.
The article explains the Goertzel algorithm for efficiently detecting Bell 202 AFSK tones (1200/2200 Hz) on small embedded systems for packet radio decoding, providing visualizations and context.
Microsoft Research releases Skala, a deep-learning exchange-correlation functional for DFT that achieves 2.8 kcal/mol accuracy on GMTKN55 at semi-local cost, outperforming traditional functionals across broad chemistry benchmarks.