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Abstract Identifying the magnetic state of materials is of great interest in a wide range of applications, but direct identification is not always straightforward due to limitations in neutron scattering experiments. In this work, we present a machine-learning approach using decision-tree algorithms to identify magnetism from the spin-integrated excitation spectrum, such as the density of states. The dataset was generated by Hartree–Fock mean-field calculations of candidate antiferromagnetic orders on a Wannier Hamiltonian, extracted from first-principle calculations targeting BaOsO $$_3$$ 3 . Our machine learning model was trained using various types of spectral data, including local density of states, momentum-resolved density of states at high-symmetry points, and the lowest excitation energies from the Fermi level. Although the density of states shows good performance for machine learning, the broadening method had a significant impact on the model’s performance. We improved the model’s performance by designing the excitation energy as a feature for machine learning, resulting in excellent classification of antiferromagnetic order, even for test samples generated by different methods from the training samples used for machine learning.

Cancer: How a plant metabolite analog suppresses tumor growth A synthetic analog of a chemical found in fruit suppresses tumor growth by targeting an RNA-binding protein (hnRNPA2B1) and preventing the production of a pro-cancer regulatory factor. Nak-Kyun Soung from the Korea Research Institute of Bioscience and Biotechnology, Cheongju, South Korea, and coworkers built on their previous discovery that a compound derived from a medicinal plant metabolite can suppress the activity of hypoxia-inducible factor-1α (HIF-1α). This protein, which is involved in many aspects of cancer biology, is activated in the low-oxygen microenvironments found inside tumors. The researchers show that the compound binds to a protein that helps with the conversion of HIF-1α–encoding RNA transcripts into HIF-1α proteins. Liver cancer cells treated with the compound grew slowly and produced less HIF-1α under both normal and low-oxygen culture conditions, highlighting the potential of this anti-cancer strategy.