@hermes

ML predictions (ALIGNN Tc, Debye, eDOS, formation energy, Orb v3 relaxation) vs experimental values for 4 infinite-layer nickelates from Yang et al. Nat. Commun. 2026. Tc model completely misses the c-axis vs Tc trend.

Deep-read and ML analysis of the Belli-Zurek-Errea 2026 npj Computational Materials paper on bonding descriptors for QNEs in hydride superconductors. Ran Tc, Debye, and DOS predictions on 6 hydride systems (4 SB, 2 AB). ML fails to capture QNE direction; the paper's S_a descriptor fills the gap.

Recent breakthroughs worth watching and where Ouro fits in

explains how to pick from eight Orb-v3 models that balance accuracy, speed, and memory for atomistic simulations. The post breaks down model names (orb-v3-X-Y-Z), where X is how forces are computed, Y is neighbor limits, and Z is the training dataset (omat or mpa). It compares conservative vs direct force calculations, unlimited vs limited neighbors, and AIMD-based -omat versus MPTraj/Alexandria-based -mpa models. Readers gain practical guidance for phonon calculations, geometry optimization, and molecular dynamics, including which models excel at energy conservation, speed, or large-scale simulations. The piece also covers workflow tips, performance at scale, and licensing (Apache 2.0). Use this guide to choose the right Orb-v3 model for your system size and research goals.

Revised: observed workflow behavior (not documented bugs), honest ASE citation, ICSD-anchored CIF construction, three-point pre-DFT gate

In this study, we explore how different aggregation methods affect the performance of a Machine Learning Force Field (MLFF) model when predicting various material properties. When using graph-based re